Implementation of Lexicon-Based and SVM Methods in Sentiment Analysis of Sayurbox App Users
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Abstract
The ever-growing technology certainly produces a large amount of data, which can provide useful information if analyzed and used properly. The purpose of this research is to analyze user sentiment towards the Sayurbox application on the Google Play Store with a Lexicon-Based approach and the Support Vector Machine (SVM) algorithm. User review data is obtained through web scraping with a total of 16,468 reviews. After preprocessing and sentiment labeling, training and test data were divided. The results showed that SVM achieved accuracy, recall, and precision of 94%, 96%, and 96% respectively, with 9 prediction errors. The model tends to predict reviews as positive sentiment, indicating user satisfaction with Sayurbox's product service, delivery, quality, and price. The findings make a contribution to the understanding of user sentiment in e-commerce services and can assist Sayurbox in improving their user experience.
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