ArticleHealthcare (Basel, Switzerland)2023
A Study on Online Health Community Users' Information Demands Based on the BERT-LDA Model.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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Who cites it
6 citing papers in PubMed.
- Evolution of Discourse in the Reddit Hemorrhoid Community: Topic and Sentiment Analysis.Journal of medical Internet research · 2026Article
- Topic modeling of workplace bullying discourse following legal regulation in South Korea.Frontiers in public health · 2026Article
- Multidimensional analysis of childbirth experiences on Chinese social media: an integrated approach using LDA, sentiment analysis and deep learning.Frontiers in public health · 2026Article
- MediNet: ensemble transfer learning approach for classification of medical drugs-related text reviews using significant combined-embeddings.BioData mining · 2025Article
- Improving optimal prompt learning through multilayer fusion and latent dirichlet allocation.Frontiers in robotics and AI · 2025Article
- Measurement and evolution of government attention to the health industry in China based on the BERTopic model.PloS one · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
As the economy and society develop and the standard of living improves, people's health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT's semantic comprehension with LDA's topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users' emotional reactions and presents valuable insights for delivering personalized health information in online communities.
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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.