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Rescue agencies like fire departments, police departments, and health departments need to identify the important and emergency messages to provide better services to the people. In generally those departments will receive a lot of messages which consist of several categories like spam, invalid etc. These departments need a system that can categorize the received messages into categories like important, invalid and spam. This paper proposes a methodology using a naïve Bayes algorithm integrated with a natural language toolkit. The proposed methodology classifies the received messages into alter1, alert2, alert3, invalid and spam messages. Depending upon the category of messages the rescue agencies will take necessary action. The proposed methodology helps the rescue agencies in saving time by taking action against important messages and ignoring unimportant messages. The system is trained through the preprocessed data. This will help the system in categorizing the test data more accurately. The accuracy of the classification is compared with the existing methodologies. The proposed methodology showed 97% accuracy in the classification of the messages into the different categories. [ABSTRACT FROM AUTHOR]