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Paper Abstract and Keywords
Presentation 2021-02-18 10:30
Classification of disaster tweets for damage assessment, and improvement by feature analysis
Yuto Oikawa, Ptaszynski Michal, Fumito Masui (KIT) NLC2020-22
Abstract (in Japanese) (See Japanese page) 
(in English) In extracting tweets useful in rescue missions during disasters, previous research have focused on extracting tweets containing specific addresses or locations. We assume that tweets without addresses can also be useful for disaster relief as the location can be inferred or written indirectly. In this study, we focus on extracting tweets from users who directly experienced the disaster (tweets with high directness) and classified them into three classes using BERT, based on the assumption that the tweets, when provided to rescue teams, can be useful for evaluating the disaster situation. Additionally, we performed feature analysis of the training data, which helped us update the annotation criteria, and improve the classification efficacy. The results were satisfying enough to be considered for application in efficient information extraction during disasters.
Keyword (in Japanese) (See Japanese page) 
(in English) Disaster Information / Rescue Request / Twitter / BERT / Document Classification / Text Mining / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 374, NLC2020-22, pp. 7-12, Feb. 2021.
Paper # NLC2020-22 
Date of Issue 2021-02-11 (NLC) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLC2020-22

Conference Information
Committee NLC  
Conference Date 2021-02-18 - 2021-02-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 17th Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2021-02-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Classification of disaster tweets for damage assessment, and improvement by feature analysis 
Sub Title (in English)  
Keyword(1) Disaster Information  
Keyword(2) Rescue Request  
Keyword(3) Twitter  
Keyword(4) BERT  
Keyword(5) Document Classification  
Keyword(6) Text Mining  
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Keyword(8)  
1st Author's Name Yuto Oikawa  
1st Author's Affiliation Kitami Institute of Technology (KIT)
2nd Author's Name Ptaszynski Michal  
2nd Author's Affiliation Kitami Institute of Technology (KIT)
3rd Author's Name Fumito Masui  
3rd Author's Affiliation Kitami Institute of Technology (KIT)
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Speaker Author-1 
Date Time 2021-02-18 10:30:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2020-22 
Volume (vol) vol.120 
Number (no) no.374 
Page pp.7-12 
#Pages
Date of Issue 2021-02-11 (NLC) 


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