Exploring Sentiment Trends: Deep Learning Analysis of Social Media Reviews on Google Play Store by Netizens

Rosa Eliviani; Dwi Diana Wazaumi

doi:10.59395/ijadis.v5i1.1318

Authors

Rosa Eliviani Informatics Management Study Program, Astra Polytechnic
Dwi Diana Wazaumi Informatics Management Study Program, Astra Polytechnic

DOI:

https://doi.org/10.59395/ijadis.v5i1.1318

Keywords:

Instagram, App Reviews, Sentiment Analysis, LSTM, Social Media, Google Play Store

Abstract

This study explores sentiment analysis of Instagram app reviews using Long Short-Term Memory (LSTM) algorithms. The rise of app stores has transformed digital interactions, particularly for social media apps. Leveraging LSTM, we aim to understand user sentiments expressed in Instagram application reviews, offering insights to enhance user experience and address concerns. The methodology involves data crawling, preprocessing, LSTM model training, and evaluation metrics. Our findings reveal promising results in accurately identifying user sentiments, with an accuracy of 77.77%, precision of 0.45, recall of 0.089, and F1-score of 0.15. This study underscores the importance of sentiment analysis in understanding user feedback and its implications for app development and user engagement.

Downloads

Download data is not yet available.

References

W. Zhu, S. Proksch, D. M. German, M. W. Godfrey, L. Li e S. McIntosh, "What is an app store? The software engineering perspective," Springer: Empirical Software Engineering, 2 January 2024. https://doi.org/10.1007/s10664-023-10362-3

S. A. Salam e R. Gupta, "Emotion Detection and Recognition from Text using Machine Learning," em International Journal of Computer Sciences and Engineering, Indore, India, 2018. https://doi.org/10.26438/ijcse/v6i6.341345

D. K. Sharma e A. K. Rai, "Impact of Social Media network by VIKOR MCDM Method," em International Conference on Cognitive Computing and Engineering Education (ICCCEE), India, 2023. https://doi.org/10.1109/ICCCEE55951.2023.10424505

H. Teotia, G. Shishodia, E. Tyagi, A. Prakash e S. Avasthi, "Instagram Analysis and Activity Automation: Using Python and Selenium Automation Tools," em International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), India, 2023. https://doi.org/10.1109/CICTN57981.2023.10140356

A. A. Maruf, F. Khanam, M. M. Haque, Z. M. Jiyad, M. F. Mridha e Z. Aung, "Challenges and Opportunities of Text-Based Emotion Detection: A Survey," IEEE Access, vol. 12, pp. 18416-18450, 2024. https://doi.org/10.1109/ACCESS.2024.3356357

A. Anggara, S. Widiono, A. T. Hidayat e Sutarman, "Analysis of Netizen Comments Sentiment on Public Official Statements on Instagram Social Media Accounts," International Journal of Advances in Data and Information Systems, vol. 3, nº 2, pp. 87-97, 2022. https://doi.org/10.25008/ijadis.v3i2.1244

B. Chembakottu, H. Li e F. Khomh, "A large-scale exploratory study of android sports apps in the google play store," Information and Software Technology , vol. 164, pp. 1-18, 2023. https://doi.org/10.1016/j.infsof.2023.107321

S. Hwang e S. Kim, "Google's app store policy change and the responses elicited from mobile content firms: A case study on Korea's app store ecosystem," Telematics and Informatics, vol. 72, pp. 1-15, 2022. https://doi.org/10.1016/j.tele.2022.101855

S. Sadiq, M. Umer, S. Ullah, S. Mirjalili, V. Rupapara e M. Nappi, "Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning," Expert Systems With Applications , vol. 181, pp. 1-8, 2021. https://doi.org/10.1016/j.eswa.2021.115111

D. S. Bimaputra e E. Sutoyo, "Aspect-Based Sentiment Analysis of Hotels in Bali on Tripadvisor Using BERT Algorithm," International Journal of Advances in Data and Information Systems, vol. 4, nº 1, pp. 29-40, 2023. https://doi.org/10.25008/ijadis.v4i2.1284

Y. Gao, J. Liu, P. Li e D. Zhou, "CE-HEAT: An Aspect-Level Sentiment Classification Approach With Collaborative Extraction Hierarchical Attention Network," IEEE Access, pp. 168548-168556, 2019. https://doi.org/10.1109/ACCESS.2019.2954590

A. Rozaq, Y. Yunitasari, K. Sussolaikah e E. R. N. Sari, "Sentiment Analysis of Kampus Mengajar 2 Toward the Implementation of Merdeka Belajar Kampus Merdeka Using Naïve Bayes and Euclidean Distence Methods," International Journal of Advances in Data and Information Systems, vol. 3, nº 1, pp. 30-37, 2022. https://doi.org/10.25008/ijadis.v3i1.1233

M. J. Islam, R. Datta e 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 Systems With Applications, vol. 223, pp. 1-10, 2023. https://doi.org/10.1016/j.eswa.2023.119919

M. A. Nurrohmat e A. SN, "Sentiment Analysis of Novel Review Using Long Short-Term Memory Method," IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 13, nº 3, pp. 209-218, 2019. https://doi.org/10.22146/ijccs.41236

U. B. Mahadevaswamy e S. P., "Sentiment Analysis using Bidirectional LSTM Network," em International Conference on Machine Learning and Data Engineering, 2023. https://doi.org/10.1016/j.procs.2022.12.400

A. C. M. V. Srinivas, Ch.Satyanarayana, Ch.Divakar e K. P. Sirisha, "Sentiment Analysis using Neural Network and LSTM," em IOP Conf. Series: Materials Science and Engineering, 2020. https://doi.org/10.1088/1757-899X/1074/1/012007

Y. Huang, Q. Liu, H. Peng, J. Wang, Q. Yang e D. Orellana-Martín, "Sentiment classification using bidirectional LSTM-SNP model and attention mechanism," Expert Systems With Applications, vol. 21, pp. 1-7, 2023. https://doi.org/10.1016/j.eswa.2023.119730

D. G. S. N. Murthy, S. R. Allu e B. Andhavarapu, "Text based Sentiment Analysis using LSTM," International Journal of Engineering Research & Technology (IJERT), vol. 9, nº 5, pp. 299-303, 2020. https://doi.org/10.17577/IJERTV9IS050290

K. A. Manurung e K. M. Lhaksmana, "Sentiment Analysis of Tourist Attraction Review from TripAdvisor Using CNN and LSTM," Intl. Journal on ICT, vol. 9, nº 1, pp. 73-85, 2023. https://doi.org/10.21108/ijoict.v9i1.756

R. Eliviani e Y. Bandung, "WSN-IoT Forecast: Wireless Sensor Network Throughput Prediction Framework in Multimedia Internet of Things," J. ICT Res. Appl., vol. 17, nº 3, pp. 336-355, 2023. https://doi.org/10.5614/itbj.ict.res.appl.2023.17.3.4

R. Mitchell, Web Scraping with Python: Collecting More Data From The Modern Web, Sebastopol: O'Reilly Media, Inc., 2018.

W. A. Wahyuni, S. Saepudin e F. Sembiring, "Sentiment Analysis of Online Investment Applications on Google Play Store using Random Forest Algorithm Method," Jurnal Mantik, vol. 4, pp. 2203-2209, 2022.

A. Tawakuli e T. Engel, "Towards Normalizing The Design Phase of Data Preprocessing Pipelines For IoT Data," em IEEE International Conference on Big Data (Big Data), 2022. https://doi.org/10.1109/BigData55660.2022.10020312

J. Hartmann, M. Heitmann, C. Siebert e C. Schamp, "More than a Feeling: Accuracy and Application of Sentiment Analysis," International Journal of Research in Marketing, vol. 40, pp. 75-87, 2022. https://doi.org/10.1016/j.ijresmar.2022.05.005

H. Dalianis, Clinical Text Mining: Secondary Use of Electronic Patient Records, Cham, Switzerland: Springer International Publishing AG part of Springer Nature, 2018.