Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies-Reference-Cited by-同舟云学术

Deep learning for real-time social media text classification for situation awareness – using Hurricanes Sandy, Harvey, and Irma as case studies

Published:2019-02-10 Issue:11 Volume:12 Page:1230-1247
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Yu Manzhu¹,Huang Qunying²,Qin Han¹³,Scheele Chris²,Yang Chaowei¹

Affiliation:

1. Department of Geography and Geoinformation Science, George Mason Univeristy, Fairfax, VA, USA

2. Department of Geography, University of Wisconsin–Madison, Madison, WI, USA

3. Ankura, Washington, DC, USA

Funder

Division of Computer and Network Systems

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2019.1574316

Reference43 articles.

1. Text Mining in Social Networks

2. Aipe, A., N. S. Mukuntha, A. Ekbal, and S. Kurohashi. 2018. “Deep Learning Approach towards Multi-label Classification of Crisis Related Tweets.” In Proceedings of the 15th ISCRAM Conference, edited by Kees Boersma and Brian Tomaszewski, Rochester, NY, USA May 2018.

3. Caragea, C., A. Silvescu, and A. H. Tapia. 2016, May. “Identifying Informative Messages in Disaster Events Using Convolutional Neural Networks.” International Conference on Information Systems for Crisis Response and Management, 137–147.

4. Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study

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