基于BiLSTM-CAM的新闻文本分类研究

A Study on News Text Classification Based on BiLSTM-CAM

摘要: 随着信息技术的迅速发展，新闻文本的数量急剧增加，怎么有效地对这些文本进行分类已成为一个重要的研究课题，新闻文本的多样性和复杂性使得传统的文本分类方法面临诸多挑战。本文提出一种BiLSTM-CAM模型旨在解决深度学习算法存在的问题，其在处理序列数据方面的优势，能够有效捕捉文本中的上下文信息，更好地识别新闻文本中的重要特征。在实验中，我们首先对数据集进行了预处理，包括文本清洗、分词和向量化等步骤，对BiLSTM-CAM模型进行了训练和调优。进行了对比实验与消融实验，研究结果表明，所提出的模型在处理数据时，能够有效提升分类性能，改善了传统方法的不足。

Abstract: With the rapid development of information technology, the volume of news texts has surged dramatically. How to effectively classify these texts has become a significant research topic; however, the diversity and complexity of news texts pose numerous challenges to traditional text classification methods. This paper proposes a BiLSTM-CAM model designed to address the limitations of deep learning algorithms. Leveraging the advantages of deep learning in processing sequential data, this model can effectively capture contextual information within the text and better identify key features in news texts. In the experiments, we first preprocessed the dataset, including steps such as text cleaning, word segmentation, and vectorization, and then trained and fine-tuned the BiLSTM-CAM model. We conducted comparative and ablation experiments. The results indicate that the proposed model effectively improves classification performance when processing data, addressing the shortcomings of traditional methods.