Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization

Abstract

Deep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data. However, Deep Belief Network has a drawback of being sensitive to hyperparameters. DBN has several hyperparameters that need to be chosen appropriately for the network to function effectively. Poor hyperparameter choices can lead to unstable training or poor performance. Therefore, the Particle Swarm Optimization algorithm is used to search for the best hyperparameters, which can lead to stable training and improved performance. The purpose of this study is to analyze public sentiment on Twitter using the Deep Belief Network method and to optimize it using Particle Swarm Optimization. The evaluation results obtained in this study are 71.4% accuracy, 71.7% precision, 71.4% recall, and 71.2% F1-score in the Deep Belief Network model which is optimized by the Particle Swarm Optimization algorithm, whereas when compared with the Deep Belief Network model alone gets evaluation results of 68.3% Accuracy, 69.0% Precision, 68.3% Recall and 68.0% F1-score. These results indicate that the use of the Particle Swarm Optimization algorithm is quite influential in analyzing sentiment in text.