CFP last date
15 July 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
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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