ANALYSIS SENTIMENT ON AIRLINE CUSTOMER SATISFACTION USING RECURRENT NEURAL NETWORK

Abstract

When talking about customer satisfaction, Twitter as a large and great media could be used to get sentiment or opinion on a product and service of a business. The sentiment will be in a form of tweet that was posted on Twitter that referred to hot debated issues subjectively. The tweet data then will be processed using machine learning to analyze the sentiment of a certain topic. This study aimed to analyze the sentiment of the Indonesian public on one of the Indonesian airlines using Deep Learning, Recurrent Neural Network (RNN) method based on the training for Long Short-Term Memory (LSTM), validation and prediction. The tweet will be selected in the span of three years (2017-2020) through the triangulation sentence sentiment process. The LSTM model gives a result of 98.5% accuracy and 92.2% validation accuracy in the data training. Whereas, the LSTM model’s data testing gives a result of 56.5% negative sentiment higher than the positive and neutral sentiment. It could be assumed that the factors which affect the negative sentiment could be used as an input to improve any business process.