The field of natural language processing (NLP) has made significant progress with the rapid development of deep learning technologies. One of the research directions in text sentiment analysis is sentiment analysis of medical texts, which holds great potential for application in clinical diagnosis. However, the medical field currently lacks sufficient text datasets, and the effectiveness of sentiment analysis is greatly impacted by different model design approaches, which presents challenges. Therefore, this paper focuses on the medical domain, using bidirectional encoder representations from transformers (BERT) as the basic pre-trained model and experimenting with modules such as convolutional neural network (CNN), fully connected network (FCN), and graph convolutional networks (GCN) at the output layer. Experiments and analyses were conducted on the METS-CoV dataset to explore the training performance after integrating different deep learning networks. The results indicate that CNN models outperform other networks when trained on smaller medical text datasets in combination with pre-trained models like BERT. This study highlights the significance of model selection in achieving effective sentiment analysis in the medical domain and provides a reference for future research to develop more efficient model architectures.

翻译：自然语言处理领域随着深度学习技术的快速发展取得了显著进展。文本情感分析的研究方向之一是医疗文本的情感分析，其在临床诊断中具有巨大的应用潜力。然而，医疗领域目前缺乏充足的文本数据集，且不同模型设计方法对情感分析效果影响较大，这带来了挑战。因此，本文聚焦于医疗领域，采用基于Transformer的双向编码器表示（BERT）作为基础预训练模型，并在输出层实验了卷积神经网络（CNN）、全连接网络（FCN）和图卷积网络（GCN）等模块。我们在METS-CoV数据集上进行了实验与分析，以探究整合不同深度学习网络后的训练性能。结果表明，在较小的医疗文本数据集上结合BERT等预训练模型训练时，CNN模型优于其他网络。本研究突出了模型选择在实现医疗领域有效情感分析中的重要性，并为未来研究开发更高效的模型架构提供了参考。