SynthesisFrontiers in public health2025
Sentiment analysis in public health: a systematic review of the current state, challenges, and future directions.
Synthesis in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Concerns of Using Large Language Models in Health Care Research and Practice: Umbrella Review.Journal of medical Internet research · 2026Pooled it
- Temporal Analysis of Patient-Centered Sentiment in Clinical Notes for Patients With Mental Health Conditions: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Assessing Pain Catastrophizing Through Free-Text Responses: A Validation of Large Language Models.medRxiv : the preprint server for health sciences · 2026Article
- Human Papillomavirus Vaccine Discourse and Sentiment on Reddit Before and After COVID-19: Mixed Methods Retrospective Cross-Sectional Study.Journal of medical Internet research · 2026Article
- Natural language processing of patient in-session speech to predict brief motivational interviewing alcohol intervention response: an exploratory study.Alcohol and alcoholism (Oxford, Oxfordshire) · 2026Article
- Comparison of Artificial Intelligence Tools With Human Coding for Sentiment, Topic, and Thematic Analysis Tasks of Public Health Datasets During the COVID-19 Pandemic in Australia: Case Study.Online journal of public health informatics · 2026Article
- Ketamine Use in Self-Described Therapeutic Contexts: A Thematic Analysis of Reddit Posts.Behavioral sciences (Basel, Switzerland) · 2026Article
- A deep sentiment model combining ALBERT-driven context and EHO-optimized architecture.Scientific reports · 2026Article
- Using AI Chatbot to Assist Students' Behavior Management for Obesity Prevention in Middle Schools: Feasibility Study.JMIR formative research · 2026Article
- Technological advancements and the use of key performance indicators in hospital management: a critical-conceptual review.Frontiers in health services · 2026Review
- Perceptions and sentiments associated with HPV vaccine uptake among Indian Reddit users: a qualitative social media analysis.BMC public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Introduction: Sentiment analysis, using natural language processing to understand opinions in text, is increasingly relevant for public health given the volume of online health discussions. Effectively using this approach requires understanding its methods, applications, and limitations. This systematic review provides a comprehensive overview of sentiment analysis in public health, examining methodologies, applications, data sources, challenges, evaluation practices, and ethical considerations. Methods: We conducted a systematic review following PRISMA guidelines, searching academic databases through Semantic Scholar and screening studies for relevance. A total of 83 papers analyzing the use of sentiment analysis in public health contexts were included. Results: The review identified a trend toward the use of advanced deep learning methods and large language models (LLMs) for a wide range of public health applications. However, challenges remain, particularly related to interpretability and resource demands. Social media is the predominant data source, which raises concerns about data quality, bias, linguistic complexity, and ethical issues. Discussion: Sentiment analysis offers the potential for gaining public health insights but faces significant methodological, data-related, and ethical challenges. Reliable and ethical application demands rigorous validation, improved model interpretability, the development of ethical frameworks, and continued research to support responsible development and deployment.
Indexed as
Identifiers
What OpenQuestion holds
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.