Development of a Hybrid CNN-BiLSTM Architecture to Enhance Text Classification Accuracy

Abstract

Introduction: Natural Language Processing (NLP) has experienced significant advancements to address the growing demand for efficient and accurate text classification. Despite numerous methodologies, achieving a balance between high accuracy and model stability remains a critical challenge. This research aims to explore the implementation of a hybrid architecture integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) with FastText embeddings, targeting effective text classification. Method: the proposed hybrid architecture combines the CNN’s ability to capture local patterns and BiLSTM’s temporal feature extraction capabilities, enhanced by FastText embeddings for richer word representation. Regulatory mechanisms such as Dropout and Early Stopping were employed to mitigate overfitting. Comparative experiments were conducted to evaluate the performance of the model with and without Early Stopping. Results: the experimental findings reveal that the model without Early Stopping achieved a remarkable accuracy of 99 %, albeit with a higher susceptibility to overfitting. Conversely, the implementation of Early Stopping resulted in a stable accuracy of 73 %, demonstrating enhanced generalization capabilities while preventing overfitting. The inclusion of Dropout further contributed to model regularization and stability. Conclusions: this study underscores the significance of balancing accuracy and stability in deep learning models for text classification. The proposed hybrid architecture effectively combines the strengths of CNN, BiLSTM, and FastText embeddings, providing valuable insights into the trade-offs between achieving high accuracy and ensuring robust generalization. Future work could further explore optimization techniques and datasets for broader applicability.