Prediction Sentiment Analysis Grab Reviews Using SVM Linear Based Streamlit

Advances in digital technology have accelerated the transformation of online transportation services, intensifying competition and driving innovations to enhance service quality. As a leading platform in Indonesia, Grab faces various challenges, including driver service quality, payment systems, and application stability, as reflected in user reviews on Google Play Store. This study aims to gain strategic insights by evaluating a linear kernel-based Support Vector Machine (SVM) model integrated into the Streamlit platform to predict the sentiment of Grab user reviews. Data were collected via web scraping and processed using tokenization, stopword removal, and stemming techniques to improve model accuracy. The model was implemented on an interactive Streamlit website featuring two main functionalities: sentiment prediction and plot visualization. The sentiment prediction feature presents sentiment distribution, performance metrics, a confusion matrix, and a classification report, while the visualization feature displays interactive word clouds, bar charts, and pie charts. Model evaluation reveals an accuracy of 83% in the Streamlit environment. These findings are expected to contribute to developers and stakeholders in enhancing Grab services and advancing more effective sentiment prediction methods.