| Issue |
ITM Web Conf.
Volume 32, 2020
International Conference on Automation, Computing and Communication 2020 (ICACC-2020)
|
|
|---|---|---|
| Article Number | 03025 | |
| Number of page(s) | 5 | |
| Section | Computing | |
| DOI | https://doi.org/10.1051/itmconf/20203203025 | |
| Published online | 29 July 2020 | |
Classifying Informatory Tweets during Disaster Using Deep Learning
1 Department of Computer Engineering, Ramrao Adik Institute Of Technology, Nerul, Navi Mumbai, India
2 Department of Computer Engineering, Ramrao Adik Institute Of Technology, Nerul, Navi Mumbai, India
3 Department of Computer Engineering, Ramrao Adik Institute Of Technology, Nerul, Navi Mumbai, India
4 Department of Computer Engineering, Ramrao Adik Institute Of Technology, Nerul, Navi Mumbai, India
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.
© The Authors, published by EDP Sciences, 2020
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