Sentiment Analysis of ChatGPT Healthcare Discourse: Insights from Twitter Data

Abstract

This paper explores the application of Chat Generative Pretrained Transformer (ChatGPT) in the healthcare domain, introducing a sentiment analysis model to evaluate ChatGPT-related tweets in healthcare contexts. The study aims to uncover predominant sentiments, thematic content, and diverse perspectives concerning ChatGPT's integration into healthcare, utilizing an extensive dataset from Twitter comprising 10,330 healthcare-related tweets. Leveraging advanced Natural Language Processing (NLP) techniques, we systematically categorized topics and emotional content within these tweets. Additionally, we conducted a comprehensive analysis of frequently occurring words in tweets expressing positive and negative sentiments. The findings reveal that the majority of healthcare-related ChatGPT tweets express either positive or negative sentiments, with a minor proportion conveying neutral viewpoints. Furthermore, to enhance our comprehension of sentiment dynamics in healthcare discussions involving ChatGPT, we applied four machine learning classifiers Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Random Forest. Remarkably, the SVM classifier demonstrated the highest accuracy at 85.6%, affirming its efficacy in healthcare sentiment analysis. In summary, this research sheds light on prevailing sentiments and perspectives regarding ChatGPT in the healthcare sector, highlighting its predominantly positive and neutral reception on platforms like Twitter. Additionally, the success of SVM as a sentiment analysis tool underscores its potential for discerning sentiments in healthcare-related ChatGPT discussions, contributing to ongoing debates on AI integration in healthcare and guiding future endeavors in this evolving field.

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