{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12"],"submitter":["Chen X"],"pubmed_abstract":["<h4>Background</h4>Social media platforms allow individuals to openly gather, communicate, and share information about their interactions with health care services, becoming an essential supplemental means of understanding patient experience.<h4>Objective</h4>We aimed to identify common discussion topics related to health care experience from the public's perspective and to determine areas of concern from patients' perspectives that health care providers should act on.<h4>Methods</h4>This study conducted a spatiotemporal analysis of the volume, sentiment, and topic of patient experience-related posts on the Weibo platform developed by Sina Corporation. We applied a supervised machine learning approach including human annotation and machine learning-based models for topic modeling and senti"],"journal":["JMIR medical informatics"],"pagination":["e59249"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11623958"],"repository":["biostudies-literature"],"pubmed_title":["Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis."],"pmcid":["PMC11623958"],"pubmed_authors":["Shen Z","Tao Y","Zhang Y","Guan T","Chen X","Kang Y"],"additional_accession":[]},"is_claimable":false,"name":"Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis.","description":"<h4>Background</h4>Social media platforms allow individuals to openly gather, communicate, and share information about their interactions with health care services, becoming an essential supplemental means of understanding patient experience.<h4>Objective</h4>We aimed to identify common discussion topics related to health care experience from the public's perspective and to determine areas of concern from patients' perspectives that health care providers should act on.<h4>Methods</h4>This study conducted a spatiotemporal analysis of the volume, sentiment, and topic of patient experience-related posts on the Weibo platform developed by Sina Corporation. We applied a supervised machine learning approach including human annotation and machine learning-based models for topic modeling and senti","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-03-13T10:27:19.938Z","creation":"2025-04-21T21:53:51.488Z"},"accession":"S-EPMC11623958","cross_references":{"pubmed":["39612510"],"doi":["10.2196/59249"]}}