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Authors
Affiliations
1 Department of Computer Science (PG), PSGR Krishnammal College for Women, Coimbatore, IN
2 Department of Computer Science, Dr. N.G.P College of Arts and Science, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 9, No 1 (2017), Pagination: 5-10
Abstract
Intelligently extracting knowledge from social media has recently attracted great interest from the Biomedical and Health Informatics community to simultaneously improve healthcare outcomes and reduce costs using consumer-generated opinion. Social media offers opportunities for patients and doctors to share their opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. However, for traditional public health surveillance systems, it is hard to detect and monitor health related concerns and changes in public attitudes to health-related issues. To solve this problem, Multiple studies illustrated the use of information in social media to discover biomedical and health-related knowledge. Several disease-specific information exchanges now exist on Face book and other online social networking sites. These new sources of knowledge, support, and engagement have become important for patients living with disease, yet the quality and content of the information provided in these digital areas are poorly understood. The existing research methodologies are discussed with their merits and demerits, so that the further research works can be concentrated more. The experimental tests conducted were on all the research works in matlab simulation environment and it is compared against each other to find the better approach under various performance measures such as Accuracy, Precision and Recall.
Keywords
Social Media, Health Related Issues, Sentiment Classifications and SOM.
