Using Social Media Data to Monitor Natural Disaster: A Multi Dimension
Convolutional Neural Network Approach with Word Embedding

Using Social Media Data to Monitor Natural Disaster: A Multi Dimension
Convolutional Neural Network Approach with Word Embedding

natural disaster, word embedding, convolutional neural network, twitter, social media

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural
disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster
management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, noneyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location
of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word
meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can
maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and
Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new
features by combining data from all word embedding techniques. The classification model is made using three Convolutional
Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the
case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the
2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique
produces better average accuracy

Mohammad Reza Faisal1
, Irwan Budiman2
, Friska Abadi3
, Muhamma

Universitas Lambung Mangkurat

29-12-2022

Fajar bagus W

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Indonesia

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