Sentiment Analysis of the TikTok Tokopedia Seller Center Application Using Support Vector Machine (SVM) and Naive Bayes Algorithms

Abstract

The TikTok Tokopedia Seller Center application is a collaboration between TikTok and Tokopedia designed to help sellers manage their stores and boost sales. Despite offering various features, complaints about poor user experience often appear in reviews on the Google Play Store. This study aims to analyze user sentiment towards the TikTok Tokopedia Seller Center application using a dataset of 2,000 reviews, using the Support Vector Machine (SVM) and Naive Bayes algorithms to classify positive, negative, and neutral sentiments. In addition, this study also attempts to compare the effectiveness of these algorithms in sentiment analysis and evaluate the performance of two weighting methods: TF-IDF and Term Presence. The dataset used was taken by scraping review data on the Google Play Store in Python, as many as 2000 user review datasets. This study found 1,171 negative sentiments, 735 positive sentiments, and 94 neutral sentiments. The results showed that the accuracy of SVM (0.81 and 0.78) was higher than Naive Bayes (0.69 and 0.75). It is hoped that this research can help potential users to find user sentiment towards the application and provide valuable information for application developers to understand user needs and expectations so that developers can improve application features more appropriately and effectively