Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm

Abstract

The continuous emergence of online transportation service platforms is one of the effects of the ever-increasing technological advancements. One such online transportation service application, Maxim, has recently been slowly gaining ground in the ride-hailing market in Indonesia. According to data collected by one media outlet in 2022, Maxim ranks third as the most preferred online transportation platform by the public, following Gojek and Grab. This suggests that there are factors causing users to lack interest in or hesitate to use the Maxim application. On the Google Play Store, user ratings (in numerical values) and written reviews serve as reasons for the potential users lack of interest. Analyzing ratings alone is less accurate and does not provide in-depth information and meaning regarding users experiences. To understand user opinions about Maxim's service and functionality, an analysis of user reviews is crucial. Therefore, this research conducts sentiment analysis on Maxim user reviews using the Support Vector Machine (SVM) algorithm to classify reviews quickly. The reviews are categorized into two classes: positive and negative sentiment. The classification process is carried out in three scenarios with different data training and testing ratios: 60:40, 70:30, and 80:20, using a Linear kernel and hyperparameter optimization with GridSearch. The best accuracy is achieved with a 70:30 ratio, which is 89.82%. Evaluation using the confusion matrix also yields a precision of 92.66%, recall of 94.09%, and an F1 score of 93.38%. The ROC-AUC curve evaluation results in an AUC value of 0.8505. The sentiment analysis results tend to lean towards positive sentiment, indicating a high level of user satisfaction with the Maxim application. Based on these sentiment results, developers can identify what aspects of the Maxim application need to be maintained and improved.