Text Classification Based on BERT

Abstract

Text classification represents a fundamental task in natural language processing, with applications spanning sentiment analysis, topic labeling, and intent detection. This paper explores the application of the Bidirectional Encoder Representations from Transformers (BERT) model, a large-scale pre-trained language model, to advance the state-of-the-art in text classification. We systematically evaluate BERT's ability to capture deep contextualized representations of text, leveraging its transformer-based architecture to understand semantic nuances and syntactic dependencies often missed by traditional methods. Through fine-tuning on multiple benchmark datasets—including IMDB for sentiment classification and AG News for topic categorization—we demonstrate that BERT significantly outperforms previous approaches, achieving accuracy improvements of up to 4.7% over convolutional and recurrent neural network baselines. Additionally, we analyze the impact of different fine-tuning strategies, such as layer-specific learning rates and dynamic token pooling, on classification performance. The study also addresses practical challenges, including computational resource requirements and model interpretability, proposing simplified variants and attention visualization techniques to enhance usability. Our findings affirm BERT’s robustness and versatility as a backbone architecture for text classification tasks, while also highlighting pathways for future optimization in low-resource and real-time application scenarios.