ArticleMethods in molecular biology (Clifton, N.J.)2024
Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature.
Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 37 citations in OpenAlex.
- Assessing the transferability of BERT to patient safety: classifying multiple types of incident reports.BMJ health & care informatics · 2025Article
- Large Language Model Architectures in Health Care: Scoping Review of Research Perspectives.Journal of medical Internet research · 2025Article
- Human-interpretable clustering of short text using large language models.Royal Society open science · 2025Article
- Affective polarization in a word: Open-ended and self-coded evaluations of partisan affect.PloS one · 2025Article
- Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.BJR artificial intelligence · 2025Review
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Pregnancy, power, and perception: An AI analysis of Instagram sentiment on prenatal weightlifting.Digital healthArticle
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 pretrained language models and the process of validating the preliminary results using both interpretable machine learning tools, as well as a novel methodology of leveraging 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.