An Enhanced BiLSTM-Based Model with Bidirectional Attention and Ant Colony Optimization for English NLP

This study aims to overcome limitations in traditional natural language processing (NLP) models, particularly in network structure and hyperparameter tuning, which often hinder optimal performance across diverse tasks. To address these issues, the ant colony optimization (ACO) algorithm is introduced. This paper optimizes the layer count and other training hyperparameters of the Bidirectional Long Short-Term Memory (BiLSTM) network, enhancing both its flexibility and classification accuracy. To further enhance BiLSTM’s bidirectional selectivity, a bidirectional attention mechanism (BAM) is incorporated, strengthening the model’s capacity to integrate historical and future contextual information. The proposed ACO-BiLSTM-BAM model is validated on the Internet Movie Database (IMDb) movie review dataset, where it achieves a classification accuracy of 92.74%, marking a significant 12.05% improvement over the base BiLSTM model, particularly in discriminating sentiment at varied levels.