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http://hdl.handle.net/10603/382475
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DC Field | Value | Language |
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dc.coverage.spatial | Computer Science | |
dc.date.accessioned | 2022-05-26T10:31:53Z | - |
dc.date.available | 2022-05-26T10:31:53Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/382475 | - |
dc.description.abstract | Recently, 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.extent | 137 p. | |
dc.language | English | |
dc.relation | 235 | |
dc.rights | university | |
dc.title | Mining User Generated Contents of online HealthCare Forum | |
dc.title.alternative | ||
dc.creator.researcher | Suryaprabha M | |
dc.subject.keyword | Engineering and Technology | |
dc.subject.keyword | Computer Science | |
dc.subject.keyword | Computer Science Interdisciplinary Applications | |
dc.description.note | ||
dc.contributor.guide | Sarojini B | |
dc.publisher.place | Coimbatore | |
dc.publisher.university | Avinashilingam Institute for Home Science and Higher Education for Women | |
dc.publisher.institution | Department of Computer Science | |
dc.date.registered | 2015 | |
dc.date.completed | 2022 | |
dc.date.awarded | 2022 | |
dc.format.dimensions | 210 mm X 290 mm | |
dc.format.accompanyingmaterial | DVD | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Department of Computer Science |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 5.29 kB | Adobe PDF | View/Open |
02_certificate.pdf | 51.14 kB | Adobe PDF | View/Open | |
03_acknowledgement.pdf | 182.24 kB | Adobe PDF | View/Open | |
04_contents.pdf | 10.32 kB | Adobe PDF | View/Open | |
05_list of tables, figures and appendices.pdf | 118.02 kB | Adobe PDF | View/Open | |
06_chapter 1.pdf | 206.17 kB | Adobe PDF | View/Open | |
07_chapter 2.pdf | 431.95 kB | Adobe PDF | View/Open | |
08_chapter 3.pdf | 524.85 kB | Adobe PDF | View/Open | |
09_chapter 4.pdf | 1.46 MB | Adobe PDF | View/Open | |
10_chapter 5.pdf | 237.25 kB | Adobe PDF | View/Open | |
11_appendices.pdf | 428.69 kB | Adobe PDF | View/Open | |
12_bibliography.pdf | 414.14 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 19.91 kB | Adobe PDF | View/Open |
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