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Network Based Modeling and Intelligent Data Mining of Social Media for Improving Care
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Network - Based Modeling and Intelligent Data Mining of Social Media for Improving Care

Category : Data Mining


Sub Category : DOTNET


Project Code : ITDDM08


Project Abstract

NETWORK-BASED MODELING AND INTELLIGENT DATA

MINING OF SOCIAL MEDIA FOR IMPROVING CARE

 

ABSTRACT                  

            As Data Mining systems refers to the demand delivery of IT resources via the internet with pay as you go pricing. A Data offers many services to the end users such as software, infrastructure and platform go on. In this paper we study about the 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. We propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in users’ forum posts, and identifies user communities and influential users for the purpose of ascertaining user opinion of cancer treatment. We used a self-organizing map to analyze word frequency data derived from user forum posts. We then introduced a novel network-based approach for modeling user forum interactions and employed a network partitioning method based on optimizing a stability qualitymeasure.This allowed us to determine consumer opinion and identify influential users within the retrieved modules using information derived frombothword-frequency data and network-based properties. Our approach can expand research into intelligently mining social media data for consumer opinion of various treatments to provide rapid, up-to-date information for the pharmaceutical industry, hospitals, and medical staff, on the effectiveness of future treatments.

 

 

 

 

 

 

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In the existing system s is to this goal, we built networks from forum posts and their replies, while accounting for content-based grouping of posts resulting from the existing forum threads.

PROPOSED CONCEPT:-

In the proposed system propose a two-step analysis framework that focuses on positive and negative sentiment, as well as the side effects of treatment, in user forum posts, and identifies user communities (modules) and influential users for the purpose of ascertaining user opinion of cancer treatment.

EXISTING ALGORITHM:-

Greedy algorithm

PROPOSED ALGORITHM:-

Cultural algorithms

ALGORITHM DEFNITION:-

 

Greedy algorithm which uses the I-WAG structure to find the best matching candidate vertex of the highest-degree query vertex as the seed. After that, it greedily expands and matches the neighbors of the seed vertex with the query vertices using our proposed ranking function.

ALGORITHM DEFNITION:-

Algorithms can perform network-clustering, one of the fundamental tasks in network analysis. Detecting particular user communities requires identifying specific, networked nodes that will allow information extraction. Healthcare providers could use patient opinion to improve their services.

 

DRAWBACKS:-

Not so efficient if there is heavy interaction between branches

Data should be carefully maintained.

ADVANTAGES:-

This techniques is very efficient if there is heavy interaction between branches

We can store the data normally and efficiently.

 
 
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