Big Data Approach to Sentiment Analysis in Machine Learning-Based Microblogs: Perspectives of Religious Moderation Public Policy in Indonesia

Abstract

The concept of religious moderation encompasses three key aspects, namely moderate thinking and understanding, moderate behavior, and moderate religious worship. With advancements in information technology, people now have the means to express their opinions through microblogs, pertaining to issues of religious moderation initiated by the Ministry of Religion of Indonesia. This study aims to evaluate public policies introduced by the Ministry of Religion regarding religious moderation such as changes in the halal logo, transfer of authority for halal certification, and regulations on the volume of loudspeakers in the mosque. Public opinions collected as the big data to get the information about public sentiment with those issues. Sentiment analysis was conducted on three primary microblogs such as Twitter, Instagram and YouTube using six machine learning algorithms. These include Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Bagging Classifier, Random Forest, and Gradient Boosting Classifier. The test results showed the highest accuracy is Gradient Boosting reached 82.27%.