Detecting Informative Tweets during Disaster using Deep Neural Networks
The post on information tweets increased as increase of data posted on social media during a disaster. Informative tweets can give the useful information about affected people, infrastructure damage, humanitarian organizations, etc. This paper proposed a method for classifying the informative and non-informative tweets during a disaster. The proposed approach is based on the Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). CNN is used for feature extraction and ANN used as a classifier for classifying the tweets. The proposed method is tested on a real-time twitter dataset such as Hurricane Harvey 2017. Proposed method outperforms the existing methods regarding precision, recall, Fl-score and accuracy.
Convolution Neural Network , Artificial Neural Network , Disaster