Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/COMSNETS.2019.8711095 | 2019 11th International Conference on Communication Systems & Networks (COMSNETS) |
Keywords | Field | DocType |
Convolution Neural Network,Artificial Neural Network,Disaster | Computer science,Computer network,Artificial intelligence,Deep neural networks,Machine learning | Conference |
ISSN | ISBN | Citations |
2155-2487 | 978-1-5386-7903-6 | 1 |
PageRank | References | Authors |
0.35 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Madichetty Sreenivasulu | 1 | 2 | 1.37 |
| M. Sridevi | 2 | 16 | 5.07 |