Optimizing long short-term memory hyperparameter for cryptocurrency sentiment analysis with swarm intelligence algorithms

Abstract

This study investigates the application of swarm intelligence algorithms, specifically particle swarm optimization (PSO), ant colony optimization (ACO), and cat swarm optimization (CSO), to optimize long short-term memory (LSTM) networks for sentiment analysis in the context of cryptocurrency. By leveraging these optimization techniques, we aimed to enhance both the accuracy and computational efficiency of LSTM models by fine-tuning critical hyperparameters, notably the number of LSTM units. The study involved a comparative analysis of LSTM models optimized with each algorithm, evaluating performance metrics such as accuracy, loss, and execution time. Results indicate that the PSO-LSTM model achieved the highest accuracy at 86.08% and the lowest loss at 0.57, with a reduced execution time of 58.43 seconds, outperforming both ACO-LSTM and CSO-LSTM configurations. These findings underscore the effectiveness of PSO in tuning LSTM parameters and emphasize the potential of swarm intelligence for enhancing neural network performance in real-time sentiment analysis applications. This research contributes to advancing optimized deep learning techniques in high dimensional data environments, with implications for improving cryptocurrency sentiment predictions.