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

Aarthi Elangovan, Sasikala S


Recently, automating the detection and classification of tweets using machine learning methods has been a tremendous help in crises. 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 EWECNN method comprises an Enhanced ELMo module to handle 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. Each of these functional modules is purportedly designed to improve the classification. Among the various approaches discussed, the proposed method outperforms the classification of microblog texts with the accuracy as 93.46 percent and the  F1-Score as 92.99 percent for multi-classification of tweets which is higher compared with other methods. The proposed multi-label classification of disaster-related text facilitates faster rescue action in a crisis scenario

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