Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media
Fahmi Candra Permana, Yusep Rosmansyah, Atje Setiawan Abdullah · Journal of Physics Conference Series · 2017
Students activity on social media can provide implicit knowledge and new perspectives for an educational system. Sentiment analysis is a part of text mining that can help to analyze and classify the opinion data. This research uses text mining and naive Bayes method as opinion classifier, to be used as an alternative methods in the process of evaluating studentss satisfaction for educational institution. Based on test results, this system can determine the opinion classification in Bahasa Indonesia using naive Bayes as opinion classifier with accuracy level of 84% correct, and the comparison between the existing system and the proposed system to evaluate students satisfaction in learning process, there is only a difference of 16.49%.