Social media community using optimized clustering algorithm
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.
Features
- User Registration: User can register himself by entering his personal details. System will provide credentials. User can use those credentials to login to the system.
- User Login: User can use his credentials to login to the system and can access user modules.
- Add Post: User can post any news or issues for discussion.
- Participate in Discussion: User can view post of other users and can participate in discussion. There can be different kind of users.
- View Post: User can view post for discussion.’
- Categorize the Group: K-Means algorithm is used to cluster data and detects communities by clustering messages from large stream of social data. This module helps to identify group of people involved in discussion.
Software Requirements:
- Windows 7 or higher
- Microsoft SQL Server 2008
- Visual Studio 2010
Hardware Components:
- Processor – i3
- Hard Disk – 5 GB
- Memory – 1GB RAM
Advantages of the proposed project:-
- 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.
Application:
This application can be used by many common users. They just have to register and can use the credentials to login to the system.