As the outgrowth of the internet as well as the social networks like twitter, Facebook user may get flooded out of raw information. Twitter message is short and may not contain enough contextual information, so traditional clustering method which utilizes the traditional method like "Bag-of-words" accept some restriction. To overwhelm with this trouble, we offered an automatic text classification process. Some social networking sites have imposed limits on the no of character for the users like

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... twitter imposed limit of only 140 characters to the users to post any tweet. To classify these kinds of tweets is a tedious task or even impossible to classify. Short text is hard to sort out due to the lack of semantic information, therefore in this research paper, a novel approach is presented that incorporate the semantic database and utilized it to elicit the necessary features to separate the short text. Experimental results indicate that the proposed approach effectively classify the incoming tweets into predefined categories such as 'News', 'Events', 'Personal message' and 'Deals'.