Sentiment Analysis of Indonesian New Capitol (IKN) Tweets by Stacked Generalization of Deep Learning

Abstract

The increasing use of Twitter for conveying the general public's sentiment toward a specific public policy generates pros and cons and has led to much research in sentiment analysis. Instead of exploring the most suitable classifier for a sentiment analysis model individually, there is a trend of employing an ensemble of classifiers to improve the accuracy and performance of the model. We proposed a model, initially by training word embedding using word2vec from 12.5K Indonesian Twitter on the relocation issue of the new capitol city of Indonesia (IKN) and by utilizing CNN, Bidirectional LSTM, and MLP as the base classifiers. Finally, we performed a stack generalization ensemble technique using MLP and LR as the meta-classifiers and compared the performance of the ensemble techniques with individual base classifiers. The base classifiers take advantage of the weights the word embedding provides to do the learning process. The results show that the stacking ensemble using MLP performs slightly better than LR as the meta-classifier, with the F-1 score of 74.65% vs. 73.78%, respectively. MLP meta-classifiers also perform somewhat better than the hard and soft majority voting ensemble with difference F-1 scores of 3.75% and 2.56%, respectively. The results show that the proposed stacked generalization technique model has improved the performance of the sentiment analysis model.