ArticleJournal of medical systems2016
Mining Health Social Media with Sentiment Analysis.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review of machine learning in online personal health data.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
- Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias.International journal of nursing sciences · 2024Article
- Analyzing patients satisfaction level for medical services using twitter data.PeerJ. Computer science · 2024Article
- Multiple-Perspective Data-Driven Analysis of Online Health Communities.Healthcare (Basel, Switzerland) · 2023Article
- Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review.Journal of medical Internet research · 2023Article
- Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design.Healthcare (Basel, Switzerland) · 2022Article
- Mapping Maternal Health in the New Media Environment: A Scientometric Analysis.International journal of environmental research and public health · 2021Article
- Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence.Journal of personalized medicine · 2021Article
- Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study.JMIR medical informatics · 2021Article
- The Communication Infrastructure as a Social Determinant of Health: Implications for Health Policymaking and Practice.The Milbank quarterly · 2021Article
- Caregivers' Experience of Caring for a Family Member with Alzheimer's Disease: A Content Analysis of Longitudinal Social Media Communication.International journal of environmental research and public health · 2020Article
- Study on Differences between Patients with Physiological and Psychological Diseases in Online Health Communities: Topic Analysis and Sentiment Analysis.International journal of environmental research and public health · 2020Article
- Sentiment Analysis in Health and Well-Being: Systematic Review.JMIR medical informatics · 2020Review
- Artificial Intelligence for Participatory Health: Applications, Impact, and Future Implications.Yearbook of medical informatics · 2019Review
- Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations.Journal of medical Internet research · 2019Article
- Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.International journal of environmental research and public health · 2018Article
- Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System.International journal of environmental research and public health · 2018Article
- Classifying and Summarizing Information from Microblogs During Epidemics.Information systems frontiers : a journal of research and innovation · 2018Article
- Comment Topic Evolution on a Cancer Institution's Facebook Page.Applied clinical informatics · 2017Article
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
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development of the Internet, more and more users utilize health communities (known as forums) to find health-related information, share their medical stories and experiences, or interact with other people in the communities. In this paper, we propose a framework to analyze the user-generated contents in a health community. The proposed framework contains three phases. First, we extract medical terms, including conditions, symptoms, treatments, effectiveness and side effects to form a virtual document for each question in the community. Next, we modify Latent Dirichlet Allocation (LDA) by adding a weighted scheme, called conLDA, to cluster virtual documents with similar medical term distributions into a conditional topic (C-topic). Finally, we analyze the clustered C-topics by sentiment polarities, and physiological and psychological sentiment. The experiment results show that conLDA outperforms the original LDA, and can cluster relevant medical terms and relevant questions together. The C-topics clustered by conLDA are more thematic than those clustered by the original LDA. The results of sentiment analysis may provide a quick reference and valuable insights for patients, caregivers and doctors.
Indexed as
Identifiers
27663246What 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.