The use of disaster hashtags especially Twitter does not have a standard format, this makes it difficult to search and collect data for emergency response. OCHA (Office for Coordination of Humanitarian Affairs) proposes to standardize the hashtag for emergency response. This study was conducted to generate automatic disaster hashtags in accordance with the OCHA standards. This research used a word representation method with Skip Gram model and SMOTE filter for handling imbalanced datasets. Then the classification of tweets and identifiers of named entities was carried out using the Conditional Random Field (CRF) model. The results showed that the use of Skip-Gram models was able to improve accuracy. The highest average accuracy of 83.9695 % was obtained by using instance-based learning with k 15 and the introduction of an entity named with recall 70.3%, precission 89.4% dan f-measure 77.1%. Automatic hashtag generation has good results with an average of 61.2% recall, 87.4% precision and 66.9% f-measure.

27/11/2018

978-602-53524-0-9

© The authors.
This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

559 times.