Authors: Ma’Shum Abdul Jabbar ; Martin Roestamy ; Himmatul Miftah ; Irman Suherman ; Muhammad Encep ; Bobi Kurniawan Soegoto
DOI: 10.1109/INCITEST64888.2024.11121469
Abstract
This study aims to overcome the challenges in selecting the most efficient machine learning model to analyze user sentiment towards the BRImo application on the Google Play Store. User review data on BRImo, an application owned by PT Bank Rakyat Indonesia, was collected through web scraping techniques from a total of 1.55 million reviews, and then 150,000 relevant samples were used. The results showed that Support Vector Machine (SVM) had the highest accuracy rate (87.13%) but required the longest training time (1310.70 seconds). In contrast, Naive Bayes had the shortest training time (0.59 seconds) but the lowest accuracy (75.00%). Logistic Regression emerged as a model that provides a balance between high accuracy and fast execution time. The contribution of this study is to provide a comprehensive guide to the efficiency and reliability of models in sentiment analysis, which can be used to assist in selecting the right model in new user review analysis. © 2024 IEEE.
Author keywords
BRImo; machine learning; sentiment analysis; web scraping
Indexed keywords
Engineering controlled terms
Barium compounds; Efficiency; Intelligent systems; Learning systems; Logistic regression; Reliability analysis; Reviews; Support vector regression
Engineering uncontrolled terms
BRImo; Comparatives studies; High-accuracy; Machine learning models; Machine-learning; Model efficiency; Sentiment analysis; Training time; User reviews; Web scrapings
Engineering main heading
Sentiment analysis
This article can be accessed at https://www.scopus.com/pages/publications/105015736072