Abstract:
Microblogging sites like Twitter are increasingly being used for
aiding relief operations during disaster events. In such situations,
identifying actionable information like needs and availabilities of
various types of resources is critical for effective coordination of
post disaster relief operations. However, such critical
information is usually submerged within a lot of conversational
content, such as sympathy for the victims of the disaster. Hence,
automated IR techniques are needed to find and process such
information. In this paper, we utilize word vector embeddings
along with fastText sentence classification algorithm to perform
the task of classification of tweets posted during natural
disasters.