Summary of A Sentiment Analysis Of Medical Text Based on Deep Learning, by Yinan Chen
A Sentiment Analysis of Medical Text Based on Deep Learning
by Yinan Chen
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the application of deep learning technologies in natural language processing (NLP) to analyze the sentiment of medical texts. The researchers utilize bidirectional encoder representations from transformers (BERT) as the base pre-trained model and experiment with various output layer modules, including convolutional neural network (CNN), fully connected network (FCN), and graph convolutional networks (GCN). They conduct experiments on the METS-CoV dataset to evaluate the training performance of different deep learning architectures. The results show that CNN models outperform other networks when trained on smaller medical text datasets in combination with pre-trained models like BERT, highlighting the importance of model selection for effective sentiment analysis in the medical domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how computers can understand and analyze the emotions expressed in medical texts. Medical texts are important because they can help doctors make diagnoses more accurately. The problem is that there aren’t many datasets available to train computer models to do this task well. This paper tries different approaches to see which ones work best when analyzing medical text data. They found that one approach, called CNN, works better than others when working with smaller datasets and a pre-trained model called BERT. This study shows how important it is to choose the right approach for analyzing emotions in medical texts and provides guidance for future research. |
Keywords
» Artificial intelligence » Bert » Cnn » Deep learning » Encoder » Gcn » Natural language processing » Neural network » Nlp
