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了解医疗保健研究人员对ChatGPT的看法:一项基于双向编码器表征变换器(BERT)情感分析和主题建模的研究。

Understanding the Perceptions of Healthcare Researchers Regarding ChatGPT: A Study Based on Bidirectional Encoder Representation from Transformers (BERT) Sentiment Analysis and Topic Modeling.

作者信息

Praveen S V, Vajrobol Vajratiya

机构信息

Department of Analytics, Xavier Institute of Management and Entrepreneurship, Bangalore, India.

Institute of Informatics and Communication, University of Delhi-South Campus, New Delhi, India.

出版信息

Ann Biomed Eng. 2023 Aug;51(8):1654-1656. doi: 10.1007/s10439-023-03222-0. Epub 2023 May 2.

Abstract

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.

摘要

在本研究中,我们运用深度学习技术来了解医疗保健领域研究人员对最近推出的聊天生成预训练变换器(ChatGPT)的看法。自ChatGPT推出以来,关于将ChatGPT用于研究目的的问题一直存在各种争论。在本文中,我们使用预训练的BERT(来自变换器的双向编码器表示)模型,对医疗保健研究人员的社交媒体帖子进行情感分析和主题建模,以了解他们对ChatGPT的看法。

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