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ii In micro-blogging services such as Twitter, the users may get overwhelmed by the raw data. One solution to this problem is the classification of Twitter messages (tweets). As short texts like tweets do not provide sufficient word occurrences, classification methods that use traditional approaches such as “Bag-Of-Words ” have limitations. To address this problem, we propose to use a small set of domain-specific features extracted from the author‟s profile and text. The proposed approach effectively classifies the text to a predefined set of generic classes such as News, Events, Opinions, Deals, and Private Messages. Existing works on classification of short text messages integrate every message with meta-information from external information sources such as Wikipedia and WordNet. Automatic text classification and hidden topic extraction approaches perform well when there is meta-information or the context of the short text is extended with knowledge extracted using large collections. But these approaches require onlin