A Review of Deep Learning-Based Text Classification Research

Abstract

The exponential growth of textual data on social media and information networks poses a significant challenge to extracting valuable information. Text classification, a core task in Natural Language Processing (NLP), is essential for organizing and categorizing such data. Deep learning has emerged as an effective approach to overcoming the limitations of traditional machine learning methods in this field. This review describes the fundamental workflow of deep learning-based text classification, including text preprocessing, feature extraction, and classifier design. It also presents a detailed overview of key technologies and conducts a comparative analysis of mainstream models using commonly used datasets to evaluate their performance. The analysis indicates that different model architectures generally exhibit scenariodependent characteristics. Pre-trained Language Models (PLMs) often show strong performance potential in short-text and context-sparse scenarios, whereas hybrid deep learning models may offer advantages in long-document modeling. Meanwhile, Graph Neural Networks (GNNs) demonstrate unique application value in processing structured text. Despite considerable progress, challenges remain in domain adaptation, few-shot learning, and model interpretability. Dependence on large-scale labeled data, together with the computational cost of complex models, is also regarded as a limitation in practical deployment. Deep learning has profoundly impacted text classification. Future research should focus on transfer learning, data augmentation, and model compression to develop more efficient, adaptable, and robust classification systems.