Aspect-Based Sentiment Analysis on Indonesian Presidential Election Using Deep Learning

Abstract

The 2019 presidential election is a presidential election that has been a hot topic of discussion for some time, and people have even talked about this topic since 2018 on the internet. In predicting the winner of the presidential election, previous research has conducted research on the aspect-based sentiment analysis (ABSA) dataset of the 2019 presidential election using machine learning algorithms such as the Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN) and produces good accuracy. This study proposes a deep learning method using the BERT (Bidirectional Encoder Representation Form Transformers) and RoBERTa (A Robustly Optimized BERT Pretraining Approach) models. The results of this study indicate that the indobenchmark BERT and RoBERTa base-Indonesian single label classification models on target features with preprocessing produce the best accuracy of 98.02%. The indolem BERT model and the indobenchmark single label classification on the target feature without preprocessing produce the best accuracy of 98.02%. The BERT indobenchmark single label classification model on aspect features with preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on aspect features without preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on the sentiment feature with preprocessing produces the best accuracy of 93.07%. The BERT indolem single label classification model on the sentiment feature without preprocessing produces the best accuracy of 94.06%. The BERT indobenchmark multi label classification model with preprocessing produces the best accuracy of 98.66%. The BERT indobenchmark multi label classification model without preprocessing produces the best accuracy of 98.66%.