CFP last date
16 December 2024
Reseach Article

Sentiment Analysis of Google Play Store Reviews using Support Vector Machines

by Mochamad Idris, Mussalimun
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 42
Year of Publication: 2024
Authors: Mochamad Idris, Mussalimun
10.5120/ijais2023451957

Mochamad Idris, Mussalimun . Sentiment Analysis of Google Play Store Reviews using Support Vector Machines. International Journal of Applied Information Systems. 12, 42 ( Jan 2024), 48-53. DOI=10.5120/ijais2023451957

@article{ 10.5120/ijais2023451957,
author = { Mochamad Idris, Mussalimun },
title = { Sentiment Analysis of Google Play Store Reviews using Support Vector Machines },
journal = { International Journal of Applied Information Systems },
issue_date = { Jan 2024 },
volume = { 12 },
number = { 42 },
month = { Jan },
year = { 2024 },
issn = { 2249-0868 },
pages = { 48-53 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number42/sentiment-analysis-of-google-play-store-reviews-using-support-vector-machines/ },
doi = { 10.5120/ijais2023451957 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-01-27T22:32:21.382779+05:30
%A Mochamad Idris
%A Mussalimun
%T Sentiment Analysis of Google Play Store Reviews using Support Vector Machines
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 42
%P 48-53
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The development of Fintech Lending-crowdfunding through the Play Store with easy access to online loan services has got a lot of attention of market segments in Indonesia in meeting their financial needs. Data mining can be used to process the reviews contained in the Fintech Igrow comments column on Google Play. The feedbacks are in the form of comments or reviews represent positive or negative sentiments. This study aims to identify and analyze by classifying public opinions into positive and negative reviews. Support Vector Machine (SVM) algorithm was chosen as a classification method. The results show that the Support Vector Machine (Linear Kernel) has the same accuracy value of 77% as the Support Vector Machine (RBFKernel). This SVM model with RBF kernel performs well in classifying positive reviews, but there is still room for improvement in terms of precision for negative sentiment classification.

References
  1. Basryah E. S., Erfina, A. dan Warman, C. 2021. Analisis sentimen aplikasi dompet digital di era 4.0 pada masa pendemi covid-19 di play store menggunakan algoritma Naive Bayes Classifier. In: Prosiding SISMATIK 2021, Sukabumi, Agustus 2021.
  2. Budi dan Mude. 2020. Perbandingan metode klasifikasi Support Vector Machine dan Naïve bayes untuk analisis sentimen pada ulasan tekstual di google play store. Jurnal Ilmiah Ilkom, 12 (2) : pp.154-161.
  3. Buntoro. 2017. Analisis sentimen calon gubernur DKI Jakarta 2017 Di Twitter. Integer Journal, 2(1), pp: 32-41
  4. Burges, Christopher J. C. 1998. A Tutorial on Support Vector Machine for Pattern Recognition”, Data Mining and knowledge Discovery 2:121-167, 1998.
  5. Herlinawati. 2019. The effect of entrepreneurial orientation on SMEs business performance Indonesia. Journal of Entrepreneurship Education, Volume 22, Issue 5.
  6. Ikhsan, R. B, Saraswati, L. A., Muchardie, B. G., Vional and Susilo, A, 2019. The determinants of students' perceived learning outcomes and satisfaction in binus online learning. In : 2019 5th International Conference on New Media Studies (CONMEDIA), October 9-11, 2019.
  7. Jiawei, Kamber, and Pei. 2012. Data Mining: Concepts and Techniques. Elsevier.
  8. Manning, C., D., Raghavan, P., & Schutze, H. 2009. An Introduction to Information Retrieval. Cambridge University Press Online Edition
  9. Miner, G., Delen, D., Nisbet, R. A. 2012. Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Elsevier.
  10. Pamungkas, F. S., dan Kharisudin. I, 2020. Analisis sentimen dengan SVM, NAIVE BAYES dan knn untuk studi tanggapan masyarakat Indonesia terhadap pandemi covid-19 pada media sosial twitter, PRISMA, Prosiding Seminar Nasional Matematika, vol. 4, p. 633,
  11. Saksonova & Merlino. 2017. Fintech as financial innovation – the possibilities and problems of implementation. European Research Studies Journal Volume XX, Issue 3A, 2017, pp:. 961-973.
  12. Sihombing, D. A., James, D. D., Massie, Merinda, H. C., Pandowo. 2021. Pengaruh brand image dan kualitas layanan terhadap keputusan pembelian layanan J&T express selama pandemi Covid-19. Jurnal EMBA Vol.10 No.1 Januari 2022, Hal. 1794-1802.
  13. Stefany dan Sari. 2019. Strategi Marketing Public Relations PT Crowde Membangun Bangsa dalam Meningkatkan Minat Investasi. PROLOGIA, 2(2):
  14. Zhou B., Wu X, Lv Z, Zhang L, Guo X. 2016. A fully automated trial selection method for optimization of motor imagery based brain-computer interface. PLoS ONE 11(9). https://doi.org/10.1371/journal.pone.0162657.
Index Terms

Computer Science
Information Sciences
The study uses Support Vector Machine (Linear Kernel) and Support Vector Machine (RBFKernel) with the Python 3.0 programming language

Keywords

Sentiment Analysis; Classification; Support Vector Machine; Google Play Store