Social Media Community Using Optimized Clustering Algorithm

Download Document/Synopsis
Now-a-days social media is used to introduce new issues and discussion on social media. More number of users participates in discussion via social media. Different users belong to different kind of groups. Positive and negative comments will be posted by the user and they will participate in discussion . Here we proposed a system to group different kind of users and system specifies from which category they belong to. For example film industry, politician etc. Once the social media data such as user messages are parsed and network relationships are identified, data mining techniques can be applied to group of different types of communities. We used K-Means clustering algorithm to cluster data. In this system we detect communities by clustering messages from large streams of social data. Our proposed algorithm gives better clustering results and provides a novel use-case of grouping user communities based on their activities. This application is used to identify group of people who viewed the post and commented on the post. This helps to categorize the users.



Advantages
  • This system helps to categorize group of people
  • This system helps to identify group of people participated in discussion
  • This system helps to approach targeted crowd.
  • We had used an effective algorithm which will provide accurate result.
Disadvantages
  • Users who don’t have internet connection can’t access the system.
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