Classifying Text Messages for Emergency Response

In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims’ requests for help. Hence, the ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. The choice of features used to encode tweets and text message data is crucial for the performance of the learning algorithms. Here, we present a comparative study of four types of feature representations used to enable learning classifiers from such data. These feature representations are obtained using a “bag of words” approach, feature abstraction, feature selection, and Latent Dirichlet Allocation (LDA). The results of our experiments on a real-world text message data set show that feature abstraction can yield better performing models than those obtained by using a “bag of words”, feature selection and LDA.