1.1
1
xml
info:srw/schema/1/mods-v3.2
Classification of Twitter Disaster Data Using a Hybrid Feature-Instance Adaptation Approach
Reza Mazloom
author
HongMin Li
author
Doina Caragea
author
Muhammad Imran
author
Cornelia Caragea
author
2018
Rochester Institute of Technology
Rochester, NY (USA)
English
Huge amounts of data that are generated on social media during emergency situations are regarded as troves of critical information. The use of supervised machine learning techniques in the early stages of a disaster is challenged by the lack of labeled data for that particular disaster. Furthermore, supervised models trained on labeled data from a prior disaster may not produce accurate results, given the inherent variation between the current and the prior disasters. To address the challenges posed by the lack of labeled data for a target disaster, we propose to use a hybrid feature-instance adaptation approach based on matrix factorization and the k nearest neighbors algorithm, respectively. The proposed hybrid adaptation approach is used to select a subset of the source disaster data that is representative for the target disaster. The selected subset is subsequently used to learn accurate Naive Bayes classifiers for the target disaster.
Tweet classification
Domain adaptation
Matrix factorization
k-Nearest Neighbors
Disaster response
exported from refbase (http://idl.iscram.org/show.php?record=2146), last updated on Mon, 25 Nov 2019 10:50:14 +0100
text
http://idl.iscram.org/files/rezamazloom/2018/2146_RezaMazloom_etal2018.pdf
RezaMazloom_etal2018
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management
Iscram 2018
Kees Boersma
editor
Brian Tomaszeski
editor
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
2018
Rochester Institute of Technology
Rochester, NY (USA)
conference publication
727
735
978-0-692-12760-5
2411-3387
1