A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification

Mohammad Reza Faisal

reza.faisal@ulm.ac.id
Lambung Mangkurat University (Indonesia)

Irwan Budiman


Lambung Mangkurat University (Indonesia)

Friska Abadi


Lambung Mangkurat University (Indonesia)

Muhammad Haekal


Lambung Mangkurat University (Indonesia)

Dodon Turianto Nugrahadi


Lambung Mangkurat University (Indonesia)

Abstract

The research aims to compare the classification performance of natural disaster messages classification from Twitter. The research experiment covers the analysis of three-word embedding-based extraction feature techniques and five different models of deep learning. The word embedding techniques that are used in this experiment are Word2Vec, fastText, and Glove. The experiment uses five deep learning models, namely three models of different dimensions of Convolutional Neural Network (1D CNN, 2D CNN, 3D CNN), Long Short-Term Memory Network (LSTM), and Bidirectional Encoder Representations for Transformer (BERT). The models are tested on four natural disaster messages datasets: earthquakes, floods, forest fires, and hurricanes. Those models are tested for classification performance


Keywords:

Twitter, natural disaster, CNN, LSTM, BERT

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Published
2023-06-30

Cited by

Faisal, M. R., Budiman, I., Abadi, F., Haekal, M., & Nugrahadi, D. T. (2023). A comparison of word embedding-based extraction feature techniques and deep learning models of natural disaster messages classification. Journal of Computer Sciences Institute, 27, 145–153. https://doi.org/10.35784/jcsi.3322

Authors

Mohammad Reza Faisal 
reza.faisal@ulm.ac.id
Lambung Mangkurat University Indonesia

Authors

Irwan Budiman 

Lambung Mangkurat University Indonesia

Authors

Friska Abadi 

Lambung Mangkurat University Indonesia

Authors

Muhammad Haekal 

Lambung Mangkurat University Indonesia

Authors

Dodon Turianto Nugrahadi 

Lambung Mangkurat University Indonesia

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