Abstract
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.
Abstract (translated)
自然语言处理（NLP）领域在深度学习技术的快速发展中取得了显著的进步。文本情感分析的一个研究方向是医疗文本的情感分析，这在临床诊断中有很大的应用潜力。然而，目前医疗领域缺乏足够的文本数据，情感分析的效果受到不同模型设计方法的影响，这带来了挑战。因此，本文重点关注医疗领域，使用来自Transformer（BERT）双向编码器表示作为基本预训练模型，并尝试在输出层使用卷积神经网络（CNN）、全连接网络（FCN）和图卷积网络（GCN）等模块。在METS-CoV数据集上进行了实验和分析，以探索在整合不同深度学习网络后进行训练的性能。实验结果表明，当用较小的医疗文本数据集与预训练模型如BERT相结合训练时，CNN模型在其他网络中表现优异。本研究突出了在医疗领域实现有效情感分析的重要性，并为未来研究提供了参考，以开发更有效的模型架构。
URL
https://arxiv.org/abs/2404.10503
