Social media text classification for crisis management

In recent years, impressive attention has been given for mining the publically available huge amount of data to gain situational awareness, which may help in preventing or decrease the effect of some disaster by taking the correct responses. In this study, an effective Convolutional Neural Networks (CNN) tweet classification system that fully supports the Turkish language has been developed. In addition, the first-ever Turkish tweet dataset for crisis response is created. This dataset has been carefully preprocessed, annotated, well organized and suitable to be used by all the well-known natural language processing tools. Furthermore, the performance of some well-known machine learning algorithms, i.e., K-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machine(SVM) was investigated. Then, the performances of the ensemble systems Random Forest (RF), AdaBoost Classifier (AdaBoost), GradientBoosting Classifier (GBC), when used for text (tweets) classification, has been also observed. A wide range of experiments was performed to investigate the performance of the developed system. As a result, the developed approach has achieved very good performance, robustness, and stability when processing both Turkish and English languages. Key Words: Crises Management Systems; Tweet Classification; Turkish language; Convolutional Neural Networks; Natural Language Processing.