Abstract:
Messages of natural disasters on social media can be used as an early warning and mitigation of natural
disasters. Researchers developed a machine learning-based classification model to identify natural disaster
messages automatically. In comparison, the Previous research used shallow learning classification
algorithms and feature extraction techniques with vector space representation. This feature extraction
technique produces high-dimensional data. This technique eliminates word order information also so that
it will lose the meaning of the sentence. Word embedding is a method for transforming the word into vectors
with numeric values that capture a word's semantic and syntactic information. We use this method to
generate structured data that keep word order, semantic, and syntactic information. The generated data are
processed using deep learning, which is 1D CNN. This learning method is generally applied in signal
classification, so we must study how to determine the input for 1D CNN to get the best accuracy. We use
several techniques to resize the number of words and three-word embedding techniques, i.e. word2vec,
Glove and fastText. We find that mean and word2vec are the resized number of word and word embedding
techniques that can give the best accuracy to classify natural disaster messages.
Keywords— social media, natural disaster, word embedding, CNN, classification
