Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM

Abstract

The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.