Abstract:
Nowadays, Twitter, Social Networking Site,
becomes most popular microblogging service and
people have started publishing data on the use of it in
natural disasters. Twitter has also created the
opportunities for first responders to know the critical
information and work effective reactions to impacted
communities. This paper presents the automated
annotation system that can detect the tweets which
contain critical information or not. Annotation is done
at tweet level with three labels by using the publicly
available annotated datasets. LibLinear classifier is
used to build a model for automatic tweets annotation.
The annotation system also creates disaster related
corpus with new tweets collected from Twitter API and
annotated on real time manner. The performance of
this model is evaluated based on different disaster
related datasets and new Myanmar_Earthquake_2016
dataset derived from Twitter. The experiments show a
high agreement rate between the annotation of this
system and the annotators.
