Implementation of Support Vector Machine Method for Twitter Sentiment Analysis Related to Cancellation of u-20 World Cup in Indonesia

Abstract

The cancellation of the U-20 world cup in Indonesia in 2023 has become a hot debate among the Indonesian people because the reasons for the cancellation are still unclear. The number of pro and con opinions uploaded by the Indonesian people on twitter social media makes these opinions can be used as data to assess opinions which are divided into three categories, namely positive, negative and neutral. After being divided into three categories, sentiment analysis will then be carried out using the SVM method and comparing linear, polynomial and rbf kernels to get the best performance of existing kernels in the support vector machine method. By using confusion matrix to measure the performance of the classification, accuracy, precision, recall and f1-score can be assessed. It was found that the 80:20 data ratio had the highest accuracy of the linear, polynomial, rbf kernel and the rbf kernel had better results than the linear and polynomial kernels, namely Accuracy 78.15%, F1-Score, 76.30%, Precision 77.37% and Recall 75.58%. In addition, the data obtained also succeeded in analyzing Indonesian texts that were input externally and categorized into positive, neutral and negative. From the results that have been obtained, the support vector machine method has been successfully implemented in sentiment analysis of the U-20 world cup cancellation in Indonesia in 2023 on twitter social media