Abstract :
Twitter is a massively popular social network website that allows users to send short messages to the general public or a set of acquaintances. The topic of these messages is range from news items to notes of a more personal nature. General, the tweet to make a summarise of and third to detecting and the monitors of the summary – based to the volume based variation to produce timeline automatically from tweet stream. Implementing continuous tweet stream reducing a text document is however not a simple task, (ie) A huge number of the tweets are worthless, nonrelated and the raucous in nature of thedue to the social nature of tweeting. Collecting tweets and extracting information from them could be very valuable in many areas including market analysis and political research. In some cases, tweets have even been used to detect where earthquakes have recently occurred. Extracting useful information from Twitter is a very challenging endeavor. This research compares using traditional clustering techniques to a simpler statistical analysis in order to group common tweets for further analysis. The research shows that the statistical approach finds a solution much quicker than a traditional clustering approach, and has similar cluster quality. At a minimum, the statistical based methods used in this research could be used to determine the number of clusters used in a traditional clustering solution and summarization of tweet streams.
Author Name : M.Arunkumar, K.Arulanandam & N.Suresh
Keywords: Clustering, Tweet Streams, Algorithm.
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