Deep Learning for Natural Language Processing: A Review of Models and Applications

This review provides a critical analysis of the transformative impact of deep learning on the advancement of Natural Language Processing ‎‎(NLP). With the increasing volume of unstructured textual data, traditional rule-based and statistical methodologies have demonstrated limitations in effectively capturing the intricacies of human language. In contrast, deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer-based architectures such as BERT and GPT, have significantly ‎enhanced NLP capabilities by facilitating context-aware, scalable, and highly accurate language comprehension. The primary objective of this ‎review is to deliver a comprehensive synthesis of deep learning architectures utilized in essential NLP tasks, including sentiment analysis, ‎text classification, machine translation, and question answering. Additionally, it examines their evolution, key applications, and comparative ‎performance across various domains. By reviewing recent literature from 2021 to 2025, this analysis also emphasizes hybrid models, multimodal systems, and adaptations for low-resource environments. The goal is to identify emerging trends, challenges (e.g., interpretability, ‎computational cost), and future directions, including data augmentation, self-supervised learning, and cross-domain generalization, ultimately-‎ly guiding researchers towards the development of more adaptive and trustworthy NLP systems‎.