SMS Classification Method for Disaster Response using Enhanced Machine Learning Algorithm

The bulk of SMS that the Quick Response Team and Rescue Agencies received during disasters made it hard for them to categorize responses based on priorities. This paper provides a method that classifies SMS received by the agency as Spam, Invalid, Alert 1 Alert 2, and Alert 3. This method allows proper response to be extended to those asking for it based on prevailing needs. This also provides a chance to ignore insignificant messages and save precious time that may be incurred by merely dealing with unimportant messages. The existing system implementation of Naïve Bayes Algorithm, a self-learning algorithm, and together with Natural Language Processing was utilized in this research. Extension of the method is however devised in order to cover the irregularity of the data to process. In the proposed system we are using SVM and Logistic Regression. Test results of the classification method showed success in its implementation and since it is a self-learning process, the method gets better and became more accurate through time.