Leveraging Sentiment Analysis in the Digital Era: Uncovering Insights from Unstructured Data for Enhanced Customer Engagement
DOI:
https://doi.org/10.71426/jmt.v2.i1.pp212-219Keywords:
Google Play Store, Sentiment Analysis, User Reviews, App Ratings, Deep Learning, Long Short-Term Memory.Abstract
The Google Play Store is a dynamic marketplace hosting a vast array of mobile applications across various categories. Analyzing user ratings and sentiments is essential for developers, marketers, and researchers to evaluate app performance and enhance user satisfaction. This study employs deep learning techniques, specifically a Long Short-Term Memory (LSTM)-based model, to examine user reviews and identify sentiment patterns. By leveraging natural language processing (NLP) and machine learning, the research investigates correlations between user feedback, app features, and overall ratings. The model processes and classifies user sentiments, such as positive, neutral, or negative and provides insights into key factors influencing user perceptions. Additionally, this study explores how app quality, functionality, and user engagement impact consumer satisfaction. Through data-driven analysis, it highlights the primary drivers of positive and negative reviews, offering a comprehensive understanding of user expectations and industry trends.
References
[1] Mustofa YA, Idris ISK. Ensemble approach to sentiment analysis of Google Play Store app reviews. Jambura Journal of Electrical and Electronics Engineering. 2024;6. Available from: https://ejurnal.ung.ac.id/index.php/jjeee/article/view/25184
[2] Fahim SF, Sounok SA, Shaeed N, Orpa MH, Niloy NT. Analyzing user sentiment in Google Play Store reviews: A natural language processing approach. In: Proceedings of the 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE; 2024. p. 1–5. Available from: https://doi.org/10.1109/ICCCNT61001.2024.10725684
[3] Aleem M, Noor MN. Predicting Android app success before Google Play Store launch using machine learning. Library Progress—Library Science, Information Technology & Computer. 2024;44(3):1907–1918. Available from: https://doi.org/10.48165/bapas.2024.44.2.1
[4] Putra RA, Novita R, Ahsyar TK, Zarnelly. Implementation of classification algorithm for sentiment analysis: Measuring app user satisfaction. Teknika. 2024;13(2):204–212. Available from: https://doi.org/10.34148/teknika.v13i2.827
[5] Santoso DB, Munna A, Untari Ningsih DH. Improved Play Store review sentiment classification accuracy with stacking ensemble. Journal of Soft Computing Exploration. 2024;5(1):38–45. Available from: https://doi.org/10.52465/joscex.v5i1.247
[6] Gopi RV, Vigna CS, Phani J, Kanth KV, Bhargavi M. Predicting app ratings on Google Play Store: An ensemble learning approach. In: Proceedings of the 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE; 2024. p. 1–5. Available from: https://doi.org/10.1109/ICITEICS61368.2024.10625269
[7] Yasin A, Fatima R, Ghazi AN, Wei Z. Python data odyssey: Mining user feedback from Google Play Store. Data in Brief. 2024;54:110499. Available from: https://doi.org/10.1016/j.dib.2024.110499
[8] Umer M, Ashraf I, Mehmood A, Ullah S, Choi GS. Predicting numeric ratings for Google apps using text features and ensemble learning. ETRI Journal. 2021;43(1):95–108. Available from: https://doi.org/10.4218/etrij.2019-0443
[9] Biswal BB, Das SN, Sahu S, Banerjee S. Play Store app rating prediction using machine learning and deep learning regression techniques. In: Proceedings of the 2023 2nd International Conference on Futuristic Technologies (INCOFT). IEEE; 2023. p. 1–4. Available from: https://doi.org/10.1109/INCOFT60753.2023.10425706
[10] Cho H, Kim S, Lee J, Lee JS. Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews. Knowledge-Based Systems. 2014;71:61–71. Available from: https://doi.org/10.1016/j.knosys.2014.06.001
[11] Minaee S, Kalchbrenner N, Cambria E, Nikzad N, Chenaghlu M, Gao J. Deep learning-based text classification: A comprehensive review. ACM Computing Surveys. 2021;54(3):1–40. Available from: https://doi.org/10.1145/3439726
[12] Yu Y. Deep learning approaches for image classification. In: Proceedings of the 2022 6th International Conference on Electronic Information Technology and Computer Engineering. 2022. p. 1494–1498. Available from: https://doi.org/10.1145/3573428.3573691
[13] Gao Y, Zheng J, Yang C, Yan R, Wang C, Tang J, Jiang Z. Gait phase detection based on hybrid deep learning for exoskeleton in outdoor activity. IEEE Access. 2025;13:39993–40011. Available from: https://doi.org/10.1109/ACCESS.2025.3544758
[14] Kumar LA, Renuka DK, Priya MS. Towards robust speech recognition model using deep learning. In: Proceedings of the 2023 International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS). IEEE; 2023. p. 253–256. Available from: https://doi.org/10.1109/ICISCoIS56541.2023.10100390
[15] Sharma M, Kandasamy I, Vasantha WB. Comparison of neutrosophic approach to various deep learning models for sentiment analysis. Knowledge-Based Systems. 2021;223:107058. Available from: https://doi.org/10.1016/j.knosys.2021.107058
[16] Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems. 2021;115:279–294. Available from: https://doi.org/10.1016/j.future.2020.08.005
[17] Malebary SJ, Abulfaraj AW. A stacking ensemble based on lexicon and machine learning methods for the sentiment analysis of tweets. Mathematics. 2024;12(21):3405. Available from: https://doi.org/10.3390/math12213405
[18] Jadhav S, Shiragapur B, Purohit R, Jha N. A systematic review on the role of sentiment analysis in healthcare. Advance Preprint. 2024. Available from: https://doi.org/10.22541/au.172516418.81416769/v1
[19] Upadhyay A, Jain H, Dhingra P, Kandhoul N, Dhurandher SK, Woungang I. Stock market prediction using social media sentiments. In: Proceedings of the International Conference on Complex, Intelligent, and Software Intensive Systems. Springer Nature Switzerland; 2024. p. 14–26. Available from: https://doi.org/10.1007/978-3-031-70011-8_2
[20] Li L, Huang Y, Ren C. Research on MOOC reviews-oriented sentiment analysis by awareness of emotional distinctions. IEEE Access. 2024;12:154823–154831. Available from: https://doi.org/10.1109/ACCESS.2024.3482232
[21] Jiang H, Guo Q, Jiang F. Text sentiment and semantic networks analysis for MOOC review texts. In: Proceedings of the 2023 International Conference on Neuromorphic Computing (ICNC). IEEE; 2023. p. 31–36. Available from: https://doi.org/10.1109/ICNC59488.2023.10462778
Published
Issue
Section
License
Copyright (c) 2025 S Rama Satyanarayana Reddy, G Neha Akshaya, O Lakshmi Koteswari, T Sreeja, Venkata Sandeep Edara (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Journal of Modern Technology publishes all articles under the Creative Commons Attribution–NonCommercial 4.0 International License (CC BY-NC 4.0). This license permits others to copy, distribute, reproduce, remix, adapt, and build upon the published work for non-commercial purposes, provided appropriate credit is given to the original authors and the source. By publishing in the Journal of Modern Technology, all authors agree to these licensing terms as a condition of publication.
