Sentiment Analysis of Tiktok Shop Closure using Naïve Bayes Algorithm and Support Vector Machine

Abstract

This research explores the closure of TikTok Shops in Indonesia triggered by the implementation of Minister of Trade Regulation (MOT) 31/2020. It focuses on analyzing user responses and sentiment towards this policy by utilizing Naïve Bayes and Support Vector Machine algorithms. While some netizens supported the move for security reasons, while others criticized it for limiting business opportunities, an analysis of 1000 datasets from Twetter with the keyword "close TikTok Shop" revealed that neutral sentiment dominated, indicating a lack of clarity or confidence regarding the reason for the closure. The results also showed that Support Vector Machine (SVM) had higher accuracy (94.4%) than Naïve Bayes (89.1%), signaling the superiority of SVM in classifying sentiment in this dataset. These findings provide deep insights into the public's perceptions and attitudes regarding the closure of TikTok Shop, providing an important basis for government and corporate understanding of the public's response to the policy