Comparison of LSTM and Naïve Bayes in Google Play Store App Review Sentiment Analysis

Endar Nirmala; Andri Fahmi

doi:10.31572/inotera.Vol11.Iss1.2026.ID653

Endar Nirmala Universitas Pamulang
Andri Fahmi Universitas Pamulang

DOI: https://doi.org/10.31572/inotera.Vol11.Iss1.2026.ID653

Keywords: Sentiment Analysis, Deep Learning, Google Play Store, LSTM, Naive Bayes

Abstract

The development of mobile application technology has driven increased user interaction through digital reviews on the Google Play Store platform. The review contains opinions that reflect the user's level of satisfaction, experience, and complaints about the app. However, the large number of reviews and variations in language expressions make manual analysis inefficient and potentially subjective. The main problem in this study is how to determine the most effective sentiment classification model to accurately identify users emotional tendencies. This study aims to compare the performance of the Naive Bayes method as a conventional machine learning model with Long Short Term Memory (LSTM) as a deep learning model based on word order in analyzing the sentiment of user reviews of Google Play Store applications. The dataset used comes from Google Play Store Reviews and goes through a pre-process process that includes text cleanup, tokenization, stopword removal, and sentiment labeling based on rating scales. The Naive Bayes model is trained using the TF-IDF representation, while the LSTM model uses an embedding sequence with standardized input padding. Evaluation uses accuracy metrics and F1-score with a ratio of 80 : 20 to train and test data distribution. The test results showed that the Naïve Bayes model achieved an accuracy of 65.78% with an F1 score of 0.5589, while the LSTM only achieved an accuracy of 45.26% with an F1-score of 0.2077. Thus, Naive Bayes was established as the best model in this study

Downloads

Download data is not yet available.

References

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, doi: 10.1007/s11042-024-19185-w.

N. Singh and U. C. Jaiswal, “Sentiment analysis on playstore user reviews of healthcare apps using deep learning techniques,” Heal. Serv. Outcomes Res. Methodol., 2025, doi: 10.1007/s10742-025-00354-9.

S. Sadiq, M. Umer, S. Ullah, S. Mirjalili, V. Rupapara, and M. Nappi, “Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning,” Expert Syst. Appl., vol. 181, p. 115111, 2021, doi: https://doi.org/10.1016/j.eswa.2021.115111.

I. Irawanto, C. Widodo, A. Hasanah, P. A. Dharma Kusumah, K. Kusrini, and K. Kusnawi, “Sentiment Analysis and Classification of Forest Fires in Indonesia,” Ilk. J. Ilm., vol. 15, no. 1, pp. 175–185, 2023, doi: 10.33096/ilkom.v15i1.1337.175-185.

M. Saymon Ahammad, S. A. Sinthia, M. Muaj Chowdhury, N.-A.-A. Asif, and M. Nurul AfsarIkram, “Sentiment Analysis of Various Ride Sharing Applications Reviews: A Comparative Analysis Between Deep Learning and Machine Learning Algorithms,” in Computational Intelligence in Data Science, M. L. Owoc, F. E. Varghese Sicily, K. Rajaram, and P. Balasundaram, Eds., Cham: Springer Nature Switzerland, 2024, pp. 434–448.

D. Khoirunnisa and E. W. Pamungkas, “Sentiment Analysis on Shopee App User Feedback: A Comparison of LSTM and BiLSTM Algorithms,” in 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML), 2025, pp. 1–5. doi: 10.1109/SIML65326.2025.11081164.

P. D. Rinanda and Mustakim, “Implementation of PNN, ANN And K-NN Algorithms on Indonesian Marketplace Reviews on Google Play Store,” in 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), 2024, pp. 1070–1074. doi: 10.1109/ICETSIS61505.2024.10459477.

A. B. Muzayyanah, R. E. Pawening, and Z. Arifin, “Analisis Sentimen Pada Ulasan Aplikasi Ehadrah Di Google Playstore Menggunakan Support Vector Machine (Svm),” IDEALIS Indones. J. Inf. Syst., vol. 7, no. 2, pp. 258–266, 2024, doi: 10.36080/idealis.v7i2.3250.

S. Bodapati, H. Bandarupally, R. N. Shaw, and A. Ghosh, “Comparison and Analysis of RNN-LSTMs and CNNs for Social Reviews Classification,” in Advances in Applications of Data-Driven Computing, J. C. Bansal, L. C. C. Fung, M. Simic, and A. Ghosh, Eds., Singapore: Springer Singapore, 2021, pp. 49–59. doi: 10.1007/978-981-33-6919-1_4.

A. Chader, L. Hamdad, and A. Belkhiri, “Sentiment Analysis in Google Play Store: Algerian Reviews Case,” in Modelling and Implementation of Complex Systems, S. Chikhi, A. Amine, A. Chaoui, D. E. Saidouni, and M. K. Kholladi, Eds., Cham: Springer International Publishing, 2021, pp. 107–121.

U. D. Gandhi, P. Malarvizhi Kumar, G. Chandra Babu, and G. Karthick, “Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM),” Wirel. Pers. Commun., 2021, doi: 10.1007/s11277-021-08580-3.

E. M. C. Trecca, A. Lonigro, M. Gelardi, B. Kim, and M. Cassano, “Mobile Applications in Otolaryngology: A Systematic Review of the Literature, Apple App Store and the Google Play Store,” Ann. Otol. Rhinol. & Laryngol., vol. 130, no. 1, pp. 78–91, 2021, doi: 10.1177/0003489420940350.

P. Gupta and S. Srivastava, “Exploring customer attitude towards neo-banking apps: a thematic analysis using Google Play and Apple App Store reviews,” Qual. Res. Financ. Mark., 2025, doi: 10.1108/QRFM-07-2024-0198.

M. Hadwan, M. Al-Sarem, F. Saeed, and M. A. Al-Hagery, “An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique,” Appl. Sci., vol. 12, no. 11, 2022, doi: 10.3390/app12115547.

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, doi: 10.1007/s11042-023-14713-6.

S. Lina, M. Sitio, and N. Rofiq, “Classification of Creditworthy Customer Using Support Vector Machine Algorithm,” vol. 10, no. 2, pp. 339–345, 2025, doi: 10.31572/inotera.Vol10.Iss2.2025.ID502.