Understanding the Perceptions of Healthcare Researchers Regarding ChatGPT: A Study Based on Bidirectional Encoder Representation from Transformers (BERT) Sentiment Analysis and Topic Modeling
In this study, we have used deep learning techniques to understand the perception of researchers in the healthcare sector about the recently introduced chat generative pre-trained transformer (ChatGPT). Ever since the launch of ChatGPT, there have been various debates over the usage of ChatGPT for r...
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Published in | Annals of biomedical engineering Vol. 51; no. 8; pp. 1654 - 1656 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.08.2023
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | In this study, we have used deep learning techniques to understand the perception of researchers in the healthcare sector about the recently introduced chat generative pre-trained transformer (ChatGPT). Ever since the launch of ChatGPT, there have been various debates over the usage of ChatGPT for research purposes. In this article, using the pre-trained BERT (Bidirectional Encoder Representations from Transformers) model, we performed sentiment analysis and topic modeling to analyze the social media posts of healthcare researchers to understand their emotions towards ChatGPT. |
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Bibliography: | SourceType-Other Sources-1 content type line 63 ObjectType-Correspondence-1 |
ISSN: | 0090-6964 1573-9686 |
DOI: | 10.1007/s10439-023-03222-0 |