Implementation of Text Mining to Detect Emotions of Fuel Price Increase Using BERT-LSTM Method

Abstract

Fuel is essential in everyday life to be used as a means of transportation, which generally uses fuel oil. In early April 2022, there was an issue that caused an uproar in Indonesian society: rising fuel prices. Based on the problems described, this research uses emotion classification with the BERT and LSTM methods using social media data, such as YouTube, to classify types of emotions based on community responses to government decisions. The highest results were obtained when oversampling was used, with an accuracy of 95%, while without oversampling, the accuracy was only 47%. In the experiments that have been carried out, it turns out that using the oversampling method and the BERT method for emotion classification will cause a reduction in accuracy for the test section.