Research on Natural Language Text Categorization Algorithm Based on Deep Learning

Natural language text categorization is one of the core tasks in Natural Language Processing (NLP), which is widely used in the fields of sentiment analysis, spam detection, news categorization and so on. Traditional text categorization methods mainly rely on manual feature engineering and shallow machine learning algorithms, which are difficult to effectively capture the semantic information of text. In recent years, the rise of deep learning methods has greatly improved the performance of text categorization, especially the pre-trained language models based on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Transformer (e.g., BERT, GPT, etc.) have outstanding performance. In this paper, we systematically study the natural language text classification algorithms based on deep learning, focusing on the text preprocessing and feature representation methods, analyzing the text classification capabilities of CNN, RNN and Transformer models, and conducting experiments on several public datasets to evaluate the performance of different algorithms. The experimental results show that the Transformer-based model performs best on the classification task, but has a higher computational cost, while CNN has a higher computational efficiency on the short text classification task.