A Multi-label Classification of Disaster-Related Tweets with Enhanced Word Embedding Ensemble Convolutional Neural Network Model

Abstract

Recently, adopting machine learning techniques to automate the identification and classification of event-related tweets has been beneficial in times of crisis. Word embeddings are the most effective word vectors for NLP processing using deep learning classifiers. This research proposes a novel method with the Enhanced Embedding from Language Model (EnELMo) for classifying tweets as different categories with higher classification accuracy and precision for the rapid rescue action in the disaster scenario. The proposed Enhanced Word Embedding Ensemble Convolutional Neural Network Model(EWECNN) method comprises an Enhanced ELMo module with crisis lexicon to create Crisis word vectors, a Novel ELMo-CNN Architecture module for feature extraction (ECA), and an effective multi-label classification of text using Crisis Word Vector specific CNN-RNN(CWV-CRNN) stacks. These functional modules are intended to improve the classification. Among the various approaches discussed, the proposed method outperforms the classification of microblog texts with an accuracy of 93.46 percent and the F1-Score of 92.99 percent for multi-classification of tweets, which is higher than other methods discussed in this study. The proposed multi-label classification of disaster-related text facilitates faster rescue action in a crisis scenario.