Classifying Informatory Tweets during Disaster Using Deep Learning

Abstract

Micro blogging platforms like Twitter generate a wealth of information during a disaster. Data can be in the form of sound, image, text, video etc. by way of tweets. Tweets produced during a disaster are not always educational. Information tweets can provide useful information about affected people, infrastructure damage, civilized organizations etc. Studies show that when it comes to sharing emergency information during a natural disaster, time is everything. Research on Twitter use during hurricanes, floods and floods provide potentially life-saving data on how information is disseminated in emergencies. The proposed system outlines how to distinguish sensitive and non-useful tweets during a disaster. The proposed method is based on the use of Word2Vec and the Convolutional Neural Network (CNN). Word2vec provides a feature vector and CNN is used to classify tweets.