Twitter, a microblogging site plays a vital role in spreading information during natural disasters. The volume of tweets posted during crisis and disaster tend to be extremely high, making it hard for disaster-affected communities and disaster management team of a local government unit to process the information in a timely manner. In this research, we describe different data mining techniques that can be used for extracting information from microblog posts that will be a basis of creating a machine learning called Disastweet: An Open-Source Tweet Mining Tool for Disaster Management. Specifically, we focus on extracting valuable information from tweets that is brief, self-contained relevant to disaster response.