Tweet analytics and tweet summarization using graph mining

Social networking sites, like Twitter, have become the fastest means of communication. Millions of user tweet everyday discussing recent and important issues. Generally, the tweets are identical or similar in nature, which causes information overload on user's wall. This makes it difficult for the user to keep a track of all the events. The best solution for this is to summarize tweets that are similar, making it easier for user to understand and decide which tweets to follow. In this paper, we present a graph based clustering technique to generate summary for tweets that are similar or identical. In addition, the paper describes about the analytics performed on tweets. Analyzing tweets help in determining the popularity of a topic and in knowing user-interested topics. Twitter analytics is the key to measure the success of the tweets posted. The proposed system gives better results compared to other existing systems.