Analysis of Zoom App User Reviews on Google Play Store Using Recurrent Neural Networks and Gated Recurrent Unit Algorithms
DOI:
https://doi.org/10.57152/predatecs.v2i2.1627Keywords:
Deep Learning, Gated Recurrent Unit, Recurrent Neural Networks, Zoom Cloud MeetingsAbstract
The World Health Organization (WHO) declared COVID-19 a global pandemic on March 11, 2020. Technology is crucial to stop the spread of the virus. Video conferencing applications such as Zoom Cloud Meetings are essential for collaboration and communication as the government issues policies to conduct various activities from home. Zoom was released in January 2013 to become a trendy video conferencing platform until now. However, post-pandemic, the Zoom App faces challenges maintaining user satisfaction due to the reduced need for virtual meetings. This research aims to analyze user reviews of the Zoom app on the Google Play Store using the RNN and Analysis of Zoom App User Reviews on Google Play Store Using Recurrent Neural Networks (RNN) and Gated Recurrent Unit Algorithms (GRU) algorithms, determine which user reviews are positive, negative, and neutral, identify common problems with Zoom for improvement recommendations, and compare the accuracy between the RNN and GRU algorithms. The results showed that out of 5000 reviews, 3728 sentiments were Positive, 1041 sentiments were Negative, and 231 sentiments were Neutral. The RNN algorithm achieved 86% accuracy, 86% precision, 100% recall, and 92% f1-score, while GRU achieved 83% accuracy, 87% precision, 92% recall, and 89% f1-score. Thus, RNN is superior in sentiment classification and most users are satisfied with the app, but negative reviews indicate areas that require improvement. This research provides valuable insights for developers to improve Zoom app features based on user feedback.
References
D. Cucinotta and M. Vanelli, “WHO declares COVID-19 a pandemic,” Acta Biomed., vol. 91, no. 1, pp. 157–160, 2020, doi: 10.23750/abm.v91i1.9397.
D. Ferdiansah, O. D. Pranata, and H. Herayati, “The EFL Students ’ Perception on Zoom Meeting as a Teaching Platform?: A Survey Research,” IDEAS J. English Lang. Teach. Learn. Linguist. Lit., vol. 12, no. 1, pp. 222–235, 2024, doi: 10.24256/ideas.v12i1.4015.
Janu Akrama Wardhana and Yuliant Sibaroni, “Aspect Level Sentiment Analysis on Zoom Cloud Meetings App Review Using LDA,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 4, pp. 631–638, 2021, doi: 10.29207/resti.v5i4.3143.
M. J. Islam, R. Datta, and A. Iqbal, “Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM,” Expert Syst. Appl., vol. 223, no. June 2022, p. 119919, 2023, doi: 10.1016/j.eswa.2023.119919.
“Analitik Zoom,” explore.zoom. Accessed: Jun. 02, 2024. [Online]. Available: https://explore.zoom.us/id/zoom-analytics/
M. A. Rizaty, “Pendapatan Zoom Naik 12,3% pada Kuartal I 2022.” Accessed: Jun. 02, 2024. [Online]. Available: https://databoks.katadata.co.id/datapublish/2022/06/21/pendapatan-zoom-naik-123-pada-kuartal-i-2022
DivaAngelia, “Platform Online Meeting Terpopuler Selama Pandemi Covid-19.” Accessed: Jun. 02, 2024. [Online]. Available: https://goodstats.id/article/platform-online-meeting-paling-populer-selama-pandemi-covid-19-oDElB
A. Haling, S. N. Ilmi, and A. D. Astuty, “Teaching Practice in a Shifting to Post-Covid 19 Era: A Study from Indonesian Higher Institution,” Qwerty. Open Interdiscip. J. Technol. Cult. Educ., vol. 0, no. 0, 2024, doi: 10.30557/qw000074.
R. Shewale, “15 Statistik Pengguna & Pendapatan Zoom (2024),” Ddemandsage. Accessed: Jun. 05, 2024. [Online]. Available: https://www.demandsage.com/zoom-statistics/
M. Iqbal, “Statistik Pendapatan dan Penggunaan Zoom (2024),” Business of Apps. Accessed: Jun. 05, 2024. [Online]. Available: https://www.businessofapps.com/data/zoom-statistics/
“Ulasan Aplikasi Zoom Meeting,” Google Play. Accessed: Jun. 05, 2024. [Online]. Available: https://play.google.com/store/apps/details?id=us.zoom.videomeetings&hl=en-ID
R. Refianti and N. Anggraeni, “Sentiment Analysis Using Convolutional Neural Network Method to Classify Reviews on Zoom Cloud Meetings Application Based on Reviews on Google Playstore,” Int. J. Eng. Sci. Inf. Technol., vol. 3, no. 3, pp. 7–16, 2023, doi: 10.52088/ijesty.v3i3.463.
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,” J. Phys. Conf. Ser., vol. 1641, no. 1, 2020, doi: 10.1088/1742-6596/1641/1/012043.
M. Razavi, H. Rastegari, A. Nabiollahi-najafabadi, and T. Javdani Gandomani, “User Reviews Classification in Play Store Applications Using Deep Learning?: An Empirical Study,” Diqiu Kexue, vol. 48, no. 6, pp. 43–57, 2023, doi: 10.3799/dqkx.2023.8163.
M. R. Raza, W. Hussain, and J. M. Merigo, “Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU,” Proc. - 2021 Innov. Intell. Syst. Appl. Conf. ASYU 2021, no. March, 2021, doi: 10.1109/ASYU52992.2021.9599044.
M. H. Shakib, “Android Malware Detection Approach’s Based on Genetic AI, CNN, RNN, LSTM, GRU, and Active Learning.,” 2023.
D. Wahyudi and Y. Sibaroni, “Deep Learning for Multi-Aspect Sentiment Analysis of TikTok App using the RNN-LSTM Method,” Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 169–177, 2022, doi: 10.47065/bits.v4i1.1665.
K. E. ArunKumar, D. V. Kalaga, C. M. S. Kumar, M. Kawaji, and T. M. Brenza, “Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells,” Chaos, Solitons and Fractals, vol. 146, p. 110861, 2021, doi: 10.1016/j.chaos.2021.110861.
Q. Liu, M. Yu, and M. Bai, “A study on a recommendation algorithm based on spectral clustering and GRU,” iScience, vol. 27, no. 2, p. 108660, 2024, doi: 10.1016/j.isci.2023.108660.
F. Shan, X. He, D. J. Armaghani, and D. Sheng, “Effects of data smoothing and recurrent neural network (RNN) algorithms for real-time forecasting of tunnel boring machine (TBM) performance,” J. Rock Mech. Geotech. Eng., vol. 16, no. 5, pp. 1538–1551, 2024, doi: 10.1016/j.jrmge.2023.06.015.
P. Mehta and S. Pandya, “A review on sentiment analysis methodologies, practices and applications,” Int. J. Sci. Technol. Res., vol. 9, no. 2, pp. 601–609, 2020.
R. Obiedat, D. Al-Darras, E. Alzaghoul, and O. Harfoushi, “Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review,” IEEE Access, vol. 9, pp. 152628–152645, 2021, doi: 10.1109/ACCESS.2021.3127140.
S. Suryani, M. F. Fayyad, D. T. Savra, V. Kurniawan, and B. H. Estanto, “Sentiment Analysis of Towards Electric Cars using Naive Bayes Classifier and Support Vector Machine Algorithm,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 1, pp. 1–9, 2023, doi: 10.57152/predatecs.v1i1.814.
N. Shah, J. Li, and T. K. Mackey, “An unsupervised machine learning approach for the detection and characterization of illicit drug-dealing comments and interactions on Instagram,” Subst. Abus., vol. 43, no. 1, pp. 273–277, 2022, doi: 10.1080/08897077.2021.1941508.
H. Lan et al., “COVID-Scraper: An Open-Source Toolset for Automatically Scraping and Processing Global Multi-Scale Spatiotemporal COVID-19 Records,” IEEE Access, vol. 9, pp. 84783–84798, 2021, doi: 10.1109/ACCESS.2021.3085682.
A. Chamekh, M. Mahfoudh, and G. Forestier, “Sentiment Analysis Based on Deep Learning in E-Commerce,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13369 LNAI, pp. 498–507, 2022, doi: 10.1007/978-3-031-10986-7_40.
K. Park, Y. Choi, W. J. Choi, H. Y. Ryu, and H. Kim, “LSTM-Based Battery Remaining Useful Life Prediction with Multi-Channel Charging Profiles,” IEEE Access, vol. 8, pp. 20786–20798, 2020, doi: 10.1109/ACCESS.2020.2968939.
N. Putu and N. Kusuma, “Prediksi Harga Saham Blue Chip Pada Indeks Idx30 Menggunakan Algoritma Recurrent Neural Network ( RNN ),” vol. 23, no. 1, pp. 90–97, 2024.
S. Mahjoub, L. Chrifi-Alaoui, B. Marhic, and L. Delahoche, “Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks,” Sensors, vol. 22, no. 11, pp. 1–20, 2022, doi: 10.3390/s22114062.
A. S. Girsang and Stanley, “Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT,” IEEE Access, vol. 11, no. September, pp. 120530–120540, 2023, doi: 10.1109/ACCESS.2023.3324535.
L. Wu, C. Kong, X. Hao, and W. Chen, “A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model,” Math. Probl. Eng., vol. 2020, 2020, doi: 10.1155/2020/1428104.
C. Zeng, C. Ma, K. Wang, and Z. Cui, “Parking Occupancy Prediction Method Based on Multi Factors and Stacked GRU-LSTM,” IEEE Access, vol. 10, pp. 47361–47370, 2022, doi: 10.1109/ACCESS.2022.3171330.
K. Karunia, A. E. Putri, M. D. Fachriani, and M. H. Rois, “Evaluation of the Effectiveness of Neural Network Models for Analyzing Customer Review Sentiments on Marketplace,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 2, no. 1, pp. 52–59, 2024, doi: 10.57152/predatecs.v2i1.1100.
A. Rolangon, A. Weku, and G. A. Sandag, “Perbandingan Algoritma LSTM Untuk Analisis Sentimen Pengguna Twitter Terhadap Layanan Rumah Sakit Saat Pandemi Covid-19,” TeIKa, vol. 13, no. 01, pp. 31–40, 2023, doi: 10.36342/teika.v13i01.3063.
M. Muta’alimah, C. K. Zarry, A. Kurniawan, H. Hasysya, M. F. Firas, and N. Nadhirah, “Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 2, no. 1, pp. 18–25, 2024, doi: 10.57152/predatecs.v2i1.1099.
