ArticleJournal of biomedical semantics2017
Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets.
Article in Journal of biomedical semantics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 110 citations in OpenAlex.
- 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
- A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Insights on HPV vaccination in the United States from mothers' comments on Facebook posts in a randomized trial.Human vaccines & immunotherapeutics · 2019Trial
- Efficient information extraction using LLMs and knowledge distillation: A study on HPV health communication.PLOS digital health · 2026Article
- Patient Voices in Dialysis Care: Sentiment Analysis and Topic Modeling Study of Social Media Discourse.Journal of medical Internet research · 2025Article
- The Role of Scientific Research in Human Papillomavirus Vaccine Discussions on Twitter: Social Network Analysis.JMIR infodemiology · 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
- Article
- Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts.Journal of medical Internet research · 2023Article
- Development of a patients' satisfaction analysis system using machine learning and lexicon-based methods.BMC health services research · 2023Article
- Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews.JMIR human factors · 2022Article
- Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning.Schizophrenia (Heidelberg, Germany) · 2022Article
- Influenza and Measles-MMR: two case study of the trend and impact of vaccine-related Twitter posts in Spanish during 2015-2018.Human vaccines & immunotherapeutics · 2022Article
- A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign.Pattern recognition · 2022Article
- Using Machine Learning-Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions.Journal of medical Internet research · 2021Article
- Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19.International journal of environmental research and public health · 2021Article
- Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.Journal of medical Internet research · 2021Article
- Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.International journal of environmental research and public health · 2021Article
- Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review.JMIR public health and surveillance · 2021Article
- Human Papillomavirus Vaccination and Social Media: Results in a Trial With Mothers of Daughters Aged 14-17.Frontiers in digital health · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAnalysing public opinions on HPV vaccines on social media using machine learning based approaches will help us understand the reasons behind the low vaccine coverage and come up with corresponding strategies to improve vaccine uptake.
objectiveTo propose a machine learning system that is able to extract comprehensive public sentiment on HPV vaccines on Twitter with satisfying performance.
methodWe collected and manually annotated 6,000 HPV vaccines related tweets as a gold standard. SVM model was chosen and a hierarchical classification method was proposed and evaluated. Additional feature sets evaluation and model parameters optimization was done to maximize the machine learning model performance.
resultsA hierarchical classification scheme that contains 10 categories was built to access public opinions toward HPV vaccines comprehensively. A 6,000 annotated tweets gold corpus with Kappa annotation agreement at 0.851 was created and made public available. The hierarchical classification model with optimized feature sets and model parameters has increased the micro-averaging and macro-averaging F score from 0.6732 and 0.3967 to 0.7442 and 0.5883 respectively, compared with baseline model.
conclusionsOur work provides a systematical way to improve the machine learning model performance on the highly unbalanced HPV vaccines related tweets corpus. Our system can be further applied on a large tweets corpus to extract large-scale public opinion towards HPV vaccines.
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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.