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A Novel Data Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin
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A Novel Data - Mining Approach Leveraging Social Media to Monitor Consumer Opinion of Sitagliptin

Category : Data Mining


Sub Category : JAVA


Project Code : ITJDM08


Project Abstract

A Novel Data-Mining Approach Leveraging Social

Media to Monitor Consumer Opinion of Sitagliptin

 

ABSTRACT
We devised a two-step analysis framework. Initial exploratory analysis usingself-organizing maps was performed to determine structures based on user opinions among the forum posts. The results were a compilation of user’s clusters and their correlated (positive or negative) opinion of the drug. Subsequent modeling using network analysis methods was used to determine influential users among the forum members. These findings can open new avenues of research into rapid data collection, feedback, and analysis.

 

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING SYSTEM:-

SOCIAL media, ranging from personal messaging to live foras, is providing limitless opportunities for patients to discuss their experiences with drugs and devices.

It is also providing limitless opportunities for companies to receive feedback on their products and services

PROPOSED SYSTEM:-

Social networks are heterogeneous, multi relational, and semi structured, making gathering such data difficult.

One method of social media data mining is link (relationship) mining, which combines social networks, link analysis, hypertext and web mining, graph mining, relational learning, and inductive logic programming

EXISTING ALGORITHM :-

Structural equation modeling (SEM)

PROPOSED   ALGORITHM :-

graph-partitioning algorithms

ALGORITHM DEFINITION:-

Structural equation modeling is generally statistical modeling  technique  which is widely used in the

Behavior sciences.it can be viewed as a combination of factor analysis and regression or path analysis.

ALGORITHM DEFINITION:-

Algorithms that cluster points using eigenvectors of matrices derived from the data. Obtain data representation in the low-dimensional space that can be easily clustered Variety of methods that use the eigenvectors differently.

DRAWBACKS:-

Since documents associated with a query, it is tend to include non- query level active learning selects all informative documents when there are a large number of documents associated with each query.

ADVANTAGES:-

A cloud storage with features to assure data confidentiality and data robustness.

Their system is a secure erasure code-based distributed storage system

 

 

 

 

 

 

 

 

 

 
 
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