SynthesisJournal of the American Medical Informatics Association : JAMIA2021
Developing a standardized protocol for computational sentiment analysis research using health-related social media data.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- The integration of artificial intelligence with social Media: opportunities, challenges, and pathways for resource optimization and doctor-patient relationship enhancement in healthcare.Frontiers in digital health · 2026Article
- 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
- Using Large Language Models for sentiment analysis of health-related social media data: empirical evaluation and practical tips.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review.Journal of nursing management · 2024Article
- Estimating Patient Satisfaction Through a Language Processing Model: Model Development and Evaluation.JMIR formative research · 2023Article
- Public Opinions toward COVID-19 Vaccine Mandates: A Machine Learning-based Analysis of U.S. Tweets.AMIA ... Annual Symposium proceedings. AMIA Symposium · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveSentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies. MATERIALS AND
methodsWe developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019.
resultsOf 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted.
conclusionsA substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.
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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.