Development of sentiment classification system for Indonesian public policy tweet

Abstract

We propose a sentiment classification system for Indonesian public policy tweet. The system consists of two subsystems: relevant tweet classification and tweet sentiment classification. Using Indonesian public policy tweet, we conduct the experiment to measure the performance for each subsystem and their combination. The purposes of the experiments are to find the best feature and algorithm for each subsystem. We emphasize to employ clustering technique for relevant tweet classification and supervised learning algorithm for sentiment classification. The best setting for clustering technique is using K-means algorithm and 2-gram feature. The best setting for tweet sentiment classification is using maximum entropy algorithm and 1-gram feature with accuracy 71.62%.