Disaster Tweet Classifications Using Hybrid Convolutional Layers and Gated Recurrent Unit

Abstract

A disaster monitoring system using Twitter data can provide information regarding disaster-prone areas and emergency response information. There have been several studies aimed at applying machine learning technologies to automatically detect disaster information from Twitter data. The Support Vector Machine (SVM) is one of the frequently used algorithms for text categorization situations, but SVM for text classification is limited by drawbacks transparency in the results caused by the high number of dimensions. Long Short-Term Memory (LSTM) is another deep learning technique that is frequently employed for text categorization, but the LSTM processing process uses quite long stages so that it requires longer computation time. The main idea in proposing this hybrid model is to combine the advantages of a highly reliable Convolutional Neural Network (CNN) architecture for handling high-dimensional data and Gated Recurrent Units (GRU) which are effective in processing sequential data and have faster computation time compared to LSTM. This study uses NLP Disaster Tweets dataset from Kaggle. The suggested model outperforms at least 12 different categories of conventional machine learning algorithms as well as other widely used deep learning models in terms of performance. The CNN-GRU hybrid model with FastText produces an accuracy of 83.32%, F1-score of 81.45%, and an AUC of 83.45%.