  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.
