A Comparative Analysis of Machine Learning Techniques for Disaster-Related Tweet Classification

Disaster-related tweets on Twitter during emergencies contain various information about injured or dead people, missing or found people, infrastructure and utility damage that can help government agencies and humanitarian organizations to priorities their help and rescue operations. Because of the huge volume of these tweets, it is essential to construct a model that can classify these tweets into different classes to better organize rescue and relief operations and save lives. In this paper, we have compared various conventional machine learning and deep learning algorithms for classifying disaster-related tweets into six different classes. The models are tested with four different disaster events such as hurricane, earthquake, flood, and wildfire to see the efficiency of the models. The range of F1-score varies from 0.61 to 0.88 for deep neural network-based models whereas it varies from 0.16 to 0.80 for the conventional machine learning classifiers. From this result, it is evident that the deep neural network models are performing significantly well in classifying disaster-related tweets even for imbalanced datasets.