Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/382475
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DC FieldValueLanguage
dc.coverage.spatialComputer Science
dc.date.accessioned2022-05-26T10:31:53Z-
dc.date.available2022-05-26T10:31:53Z-
dc.identifier.urihttp://hdl.handle.net/10603/382475-
dc.description.abstractRecently, online healthcare forums are gaining huge popularity in the healthcare domain. Health is one of people s most important concerns so that social media is widely used for seeking health-related information. The impact of the health-care industry on day-to-day patient care, and medical research is immense. Health-care and disease related posts that are effectively and efficiently beneficial to health-seekers in their information search. The proposed outcome of the research work is to provide better efficiency for user-generated contents in online health-care forums. In this research work, the data were extracted from the (MedHelp) online health-care forum. In this work, the knowledge adoption model framework is analyzed by text-mining and NLP method to get needed information that will be evaluated by SVM-RBF classification to provide adopted and not-adopted answers based on the class values to get health-care knowledge adoption decision. Stakeholder and Topics analysis in online healthcare forum is to extract the medical and users oriented keywords based on the K-medoid clustering technique. Then the cluster keywords are evaluated by performance metrics to get better results. The percentage of stakeholders that are (Patients, Caregivers, and specialists) contributing to the given messages. The percentage of topics that are (Treatments, Symptoms, Drugs, Examinations, Complications) contributing to the given messages. Finally, the sentiment analysis framework is analyzed by a sentiment lexicon-based approach to provide the percentage of the sentiment results. newlinei) Major objectives : newlineThis research aims to analyze the messages and establish the knowledge adoption framework and content analysis framework using text mining techniques for the online healthcare community. newline To determine the plausible answer to a question among the group of replies using the SVM-RBF classification method. newline To identify the stakeholders of the forum and determine the topic of discussion of the stakeholders and their contribution
dc.format.extent137 p.
dc.languageEnglish
dc.relation235
dc.rightsuniversity
dc.titleMining User Generated Contents of online HealthCare Forum
dc.title.alternative
dc.creator.researcherSuryaprabha M
dc.subject.keywordEngineering and Technology
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Interdisciplinary Applications
dc.description.note
dc.contributor.guideSarojini B
dc.publisher.placeCoimbatore
dc.publisher.universityAvinashilingam Institute for Home Science and Higher Education for Women
dc.publisher.institutionDepartment of Computer Science
dc.date.registered2015
dc.date.completed2022
dc.date.awarded2022
dc.format.dimensions210 mm X 290 mm
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File5.29 kBAdobe PDFView/Open
02_certificate.pdf51.14 kBAdobe PDFView/Open
03_acknowledgement.pdf182.24 kBAdobe PDFView/Open
04_contents.pdf10.32 kBAdobe PDFView/Open
05_list of tables, figures and appendices.pdf118.02 kBAdobe PDFView/Open
06_chapter 1.pdf206.17 kBAdobe PDFView/Open
07_chapter 2.pdf431.95 kBAdobe PDFView/Open
08_chapter 3.pdf524.85 kBAdobe PDFView/Open
09_chapter 4.pdf1.46 MBAdobe PDFView/Open
10_chapter 5.pdf237.25 kBAdobe PDFView/Open
11_appendices.pdf428.69 kBAdobe PDFView/Open
12_bibliography.pdf414.14 kBAdobe PDFView/Open
80_recommendation.pdf19.91 kBAdobe PDFView/Open


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