Twitter sentiment analysis on 2013 curriculum using ensemble features and k-nearest neighbor

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M. Rizzo Irfan, M. Ali Fauzi, Tibyani, Nurul Dyah Mentari

2018 International Journal of Electrical and Computer Engineering Vol. 8 Issue 6 Article Cited by 16 Quartile

Abstract

2013 curriculum is a new curriculum in the Indonesian education system which has been enacted by the government to replace KTSP curriculum. The implementation of this curriculum in the last few years has sparked various opinions among students, teachers, and public in general, especially on social media twitter. In this study, a sentimental analysis on 2013 curriculum is conducted. Ensemble of several feature sets were used including textual features, twitter specific features, lexicon based features, Parts of Speech (POS) features, and Bag of Words (BOW) features for the sentiment classification using K-Nearest Neighbor method. The experiment result showed that the the ensemble features have the best performance of sentiment classification compared to only using individual features. The best accuracy using ensemble features is 96% when k=5 is used. Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved.

Affiliations

Faculty of Computer Science, Brawijaya University, Jl. Veteran, Malang, Indonesia