ArticleStudies in health technology and informatics2019
How Do General-Purpose Sentiment Analyzers Perform when Applied to Health-Related Online Social Media Data?
Article in Studies in health technology and informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Developing a standardized protocol for computational sentiment analysis research using health-related social media data.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- The Role of Seed Users in Nurturing an Online Health Community for Smoking Cessation Among People With HIV/AIDS.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2024Trial
- Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication.NPJ digital medicine · 2025Article
- Using Natural Language Processing to Explore Patient Perspectives on AI Avatars in Support Materials for Patients With Breast Cancer: Survey Study.Journal of medical Internet research · 2025Article
- Large Language Model-Based Responses to Patients' In-Basket Messages.JAMA network open · 2024Article
- A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.JMIR mental health · 2024Article
- Sentiment analysis of COVID-19 cases in Greece using Twitter data.Expert systems with applications · 2023Article
- Topic Modelling and Sentiment Analysis of Tweets Related to Freedom Convoy 2022 in Canada.International journal of public health · 2022Article
- Why do people oppose mask wearing? A comprehensive analysis of U.S. tweets during the COVID-19 pandemic.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Using Natural Language Processing and Sentiment Analysis to Augment Traditional User-Centered Design: Development and Usability Study.JMIR mHealth and uHealth · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Sentiment analysis has been increasingly used to analyze online social media data such as tweets and health forum posts. However, previous studies often adopted existing, general-purpose sentiment analyzers developed in non-healthcare domains, without assessing their validity and without customizing them for the specific study context. In this work, we empirically evaluated three general-purpose sentiment analyzers popularly used in previous studies (Stanford Core NLP Sentiment Analysis, TextBlob, and VADER), based on two online health datasets and a general-purpose dataset as the baseline. We illustrate that none of these general-purpose sentiment analyzers were able to produce satisfactory classifications of sentiment polarity. Further, these sentiment analyzers generated inconsistent results when applied to the same dataset, and their performance varies to a great extent across the two health datasets. Significant future work is therefore needed to develop context-specific sentiment analysis tools for analyzing online health data.
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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.