ArticleHealthcare (Basel, Switzerland)2020
Sentiment Analysis Methods for HPV VaccinesRelated Tweets Based on Transfer Learning.
Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy.Computers in biology and medicine · 2021Pooled it
- Increasing Women's Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study.International journal of environmental research and public health · 2022Trial
- Cross-dialectal Arabic translation: comparative analysis on large language models.Frontiers in artificial intelligence · 2025Article
- Analyzing digital societal interactions and sentiment classification in Twitter (X) during critical events in Chile.Heliyon · 2024Article
- Article
- Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study.Journal of medical Internet research · 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 COP9-related tweets: a comparative study of pre-trained models and traditional techniques.Frontiers in big data · 2024Article
- Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts.Journal of medical Internet research · 2023Article
- Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework.Journal of biomedical informatics · 2023Article
- Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling.Journal of medical Internet research · 2022Article
- COVID-19 vaccine hesitancy: a social media analysis using deep learning.Annals of operations research · 2022Article
- A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.Journal of medical Internet research · 2022Article
- Studying topic engagement and synergy among candidates for 2020 US Elections.Social network analysis and mining · 2022Article
- Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.International journal of environmental research and public health · 2021Article
- Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The widespread use of social media provides a large amount of data for public sentimentanalysis. Based on social media data, researchers can study public opinions on humanpapillomavirus (HPV) vaccines on social media using machine learning-based approaches that willhelp us understand the reasons behind the low vaccine coverage. However, social media data isusually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limitsthe application of deep learning methods in effectively training models. To tackle this problem, wepropose three transfer learning approaches to analyze the public sentiment on HPV vaccines onTwitter. One was transferring static embeddings and embeddings from language models (ELMo)and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWEBiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called finetuninggenerative pre-training (GPT) and fine-tuning bidirectional encoder representations fromtransformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pretraining(GPT) model. The fine-tuned BERT model was constructed with BERT model. Theexperimental results on the HPV dataset demonstrated the efficacy of the three methods in thesentiment analysis of the HPV vaccination task. The experimental results on the HPV datasetdemonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. Thefine-tuned BERT model outperforms all other methods. It can help to find strategies to improvevaccine uptake.
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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.