Deep Learning Architectures for Text Classification

Text classification is crucial in natural language processing applications such as sentiment analysis, topic tagging, and news categorization. This paper presents a comparative analysis of three deep learning architectures—LSTM, Bidirectional LSTM, and Character-level Convolutional Neural Networks (Char-CNN), for the task of news categorization using the AG News dataset. The models were trained using a unified preprocessing pipeline, including tokenization, padding, and label encoding. Performance was evaluated based on classification accuracy, training time, and learning stability across epochs. The results show that Bidirectional LSTM outperforms the standard LSTM in capturing long-range dependencies by leveraging both past and future context. The Character-level CNN demonstrates robust performance by learning morphological patterns directly from raw text, making it resilient to misspellings and out-of-vocabulary words. The trade- offs between model complexity, training time, and interpretability has also been analyzed. This study offers practical insights into model selection for real-world NLP applications and highlights the importance of architectural choices in deep learning-based text classification.