  Social media like Twitter provide a common platform to share and communicate
personal experiences with other people. People often post their life
experiences, local news, and events on social media to inform others. Many
rescue agencies monitor this type of data regularly to identify disasters and
reduce the risk of lives. However, it is impossible for humans to manually
check the mass amount of data and identify disasters in real-time. For this
purpose, many research works have been proposed to present words in
machine-understandable representations and apply machine learning methods on
the word representations to identify the sentiment of a text. The previous
research methods provide a single representation or embedding of a word from a
given document. However, the recent advanced contextual embedding method (BERT)
constructs different vectors for the same word in different contexts. BERT
embeddings have been successfully used in different natural language processing
(NLP) tasks, yet there is no concrete analysis of how these representations are
helpful in disaster-type tweet analysis. In this research work, we explore the
efficacy of BERT embeddings on predicting disaster from Twitter data and
compare these to traditional context-free word embedding methods (GloVe,
Skip-gram, and FastText). We use both traditional machine learning methods and
deep learning methods for this purpose. We provide both quantitative and
qualitative results for this study. The results show that the BERT embeddings
have the best results in disaster prediction task than the traditional word
embeddings. Our codes are made freely accessible to the research community.
