Sentiment Classification on Twitter Social Media Using K-Means Clustering, C4.5 and Naive Bayes (Case Study: Blocking Paypal by Kominfo)

Abstract

Kominfo (Ministry of Communication and Information) requires all PSEs (Electronic System Providers) to register themselves so that their access is not blocked, as shown in the case of Paypal and several other PSEs. The blocking case reaps mixed opinions from netizens, especially Twitter social media users. We use the sentiment values obtained from the content of tweets collected through the crawling process and employ the K-Means Clustering to group them into clusters. Finally, we use these clusters as the target in a dataset and classify them using the C4.5 and Naive Bayes algorithms. Of the 1000 netizen tweets studied, we found that 6.5% of netizens supported the blocking action, 75.4% did not care or felt that the blocking action had no effect on them, and 15.4% did not support the blocking by Kominfo. The classification results in this study resulted in a 98.2% accuracy value, a 95% precision value, and a 95.5% recall value.