![](bg8.png)
mative tweet classification task. We also trained clas-
sifiers with several word embeddings, namely, Fast-
text, GloVe and BERT, as input features. More-
over, we showed that our proposed deep neural model
BERT
Hyb
is more effective in identifying informative
tweets as compared to conventional classifiers in dif-
ferent crisis related corpus from Twitter.
As future works we intend to further investigate
different deep learning models combinations and im-
plement a complete pipeline where the tweets are
crawled and classified in real time based on crisis re-
lated trending topics.
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