Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature
This provides a practical guide for researchers and practitioners using NLP for sentiment analysis in medical texts, though it appears incremental in applying existing methods to a specific domain.
The authors tackled the problem of evaluating bias in discourse about chronic Lyme disease by applying BERT and ChatGPT for sentiment analysis on 5,643 scientific abstracts, demonstrating a practical NLP workflow for the medical domain.
This chapter presents a practical guide for conducting Sentiment Analysis using Natural Language Processing (NLP) techniques in the domain of tick-borne disease text. The aim is to demonstrate the process of how the presence of bias in the discourse surrounding chronic manifestations of the disease can be evaluated. The goal is to use a dataset of 5643 abstracts collected from scientific journals on the topic of chronic Lyme disease to demonstrate using Python, the steps for conducting sentiment analysis using pre-trained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of using emerging state-of-the-art large language models like ChatGPT. This serves as a useful resource for researchers and practitioners interested in using NLP techniques for sentiment analysis in the medical domain.