Fanaticism Analysis of Social Media Using Machine Learning

Abstract

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.