Fanaticism Analysis of Social Media Using Machine Learning


  • Agus Sasmito Aribowo Universitas Pembangunan Nasional Veteran Yogyakarta
  • Nur Heri Cahyana Universitas Pembangunan Nasional Veteran Yogyakarta



fanaticism, machine learning, feature extraction, social media


Sentiment and emotion analysis on social media is an interesting study because it reveals the emotional state of the public in a domain. The challenges in sentiment analysis research in Indonesian are inefficient preprocessing, inaccurate feature extraction methods, and low classification accuracy by machine learning. One aspect of sentiment analysis is fanaticism. Fanaticism contains an emotional element in sentiment analysis. This article discusses how to detect opinions that contain political fanaticism, then categorize them into several polarities of political fanaticism. Feature extraction is done by processing sentiment, anger, happiness, disgust, surprise, fear, and hate speech analysis. Knowledge for classification is K-NN, Naive Bayes, Random Forest, and Decision Tree. The aim is to find out the best combination of machine learning methods for feature extraction and finally used for fanaticism categorization. The best method is Random Forest with an accuracy of 81% and will be used as a final method for monitoring fanaticism on social media.


A. S. Aribowo, H. Basiron, N. S. Herman, and S. Khomsah, “Fanaticism category generation using tree-based machine learning method,” Journal of Physics: Conference Series, vol. 1501, no. 1, 2020, DOI: 10.1088/1742-6596/1501/1/012021.

K. Marimaa, “The Many Faces of Fanaticism,” in ENDC Proceedings, 2011, vol. 14, pp. 29–55.

S. I. Dewi and A. Aminulloh, “Social Media : Democracy in the Shadow of Fanaticism,” in The 3rd Conference on Communication, Culture and Media Studies, 2016, pp. 79–88.

J. Laksana and A. Purwarianti, “Indonesian Twitter Text Authority Classification For Government in Bandung,” International Conference of Advanced Informatics: Concept, Theory, and Application (ICAICTA) Indonesian, pp. 129–134, 2014.

Y. E. Cakra and B. D. Trisedya, “Stock Price Prediction using Linear Regression based on Sentiment Analysis,” in ICAICS, 2015, pp. 147–154.

E. Barfian, B. H. Iswanto, and S. M. Isa, “Twitter Pornography Multilingual Content Identification Based on Machine Learning,” in International Conference on Computer Science and Computational Intelligence, 2017, vol. 116, pp. 129–136, DOI: 10.1016/j.procs.2017.10.024.

M. A. Fauzi, "Random Forest Approach for Sentiment Analysis in Indonesian Language," Indonesian Journal of Electrical Engineering and Computer Science, vol. 12, no. 1, pp. 46–50, 2018, DOI: 10.11591/ijeecs.v12.i1.pp46-50.

F. Kusuma Wardani, R. Valentinus Hananto, and V. Nurcahyawati, “Analisis Sentimen Untuk Pemeringkatan Popularitas Situs Belanja Online Di Indonesia Menggunakan Metode Naive Bayes,” JSIKA, vol. 08, no. 01, pp. 1–9, 2019.

Y. Yunitasari, A. Musdholifah, and A. K. Sari, “Sarcasm Detection For Sentiment Analysis in Indonesian Tweets,” Indonesian Journal of Computing and Cybernetics Systems, vol. 13, no. 1, pp. 53–62, 2019.

S. Khomsah and A. S. Aribowo, “Model text-preprocessing komentar Youtube dalam bahasa Indonesia,” Rekayasa Sistem dan Teknologi Informasi, RESTI, vol. 4, no. 4, pp. 648–654, 2020.

A. Almonayyes, “Multiple Explanations Driven Naive Bayes Classifier.,” Journal of Universal Computer Science, vol. 12, no. 2, pp. 127–139, 2006.

J. Kléma and A. Almonayyes, "Automatic Categorization of Fanatic Text Using Random Forests," Kuwait Journal of Science and Engineering, vol. 33, no. 2, pp. 1–18, 2006.

A. Almonayyes, “Classifying Documents By Integrating Contextual Knowledge With Boosting,” in International Conference on Artificial Intelligence and Computer Science, 2016, no. November, pp. 28–29.

[A. Almonayyes, “Tweets Classification Using Contextual Knowledge And Boosting,” International Journal of Advances in Electronics and Computer Science, no. 4, pp. 87–92, 2017.