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Classification of Twitter Disaster Data Using a Hybrid Feature-Instance Adaptation Approach
Reza Mazloom
HongMin Li
Doina Caragea
Muhammad Imran
Cornelia Caragea
Kees Boersma
Brian Tomaszeski
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.
urn:ISBN:978-0-692-12760-5
openurl:?ctx_ver=Z39.88-2004&rfr_id=info%3Asid%2Fidl.iscram.org%2F&genre=proceeding&title=Classification%20of%20Twitter%20Disaster%20Data%20Using%20a%20Hybrid%20Feature-Instance%20Adaptation%20Approach&stitle=Iscram%202018&issn=2411-3387&isbn=978-0-692-12760-5&date=2018&spage=727&epage=735&aulast=Reza%20Mazloom&au=HongMin%20Li&au=Doina%20Caragea&au=Muhammad%20Imran&au=Cornelia%20Caragea&pub=Rochester%20Institute%20of%20Technology&place=Rochester%2C%20NY%20%28USA%29&sid=refbase%3AISCRAM
url:http://idl.iscram.org/show.php?record=2146
citekey:RezaMazloom_etal2018
citation:Reza Mazloom, HongMin Li, Doina Caragea, Muhammad Imran, & Cornelia Caragea. (2018). Classification of Twitter Disaster Data Using a Hybrid Feature-Instance Adaptation Approach. In Kees Boersma, & Brian Tomaszeski (Eds.), ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management (pp. 727-735). Rochester, NY (USA): Rochester Institute of Technology.
2018
ConferencePaper
text
Tweet classification, Domain adaptation, Matrix factorization, k-Nearest Neighbors, Disaster response
file:http://idl.iscram.org/files/rezamazloom/2018/2146_RezaMazloom_etal2018.pdf
Rochester Institute of Technology
English
2411-3387
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management
2018
727
735
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