Prioritization of key objectives during floods

Abstract

Now a day social networks generates large volume of data per sec and one of such network is Twitter. Twitter is one of the popular public platforms with an extract of openly express user’s opinion. Our work aims focus on tweets generated in regard to floods and especially the tweets posed by those affected by floods so that we may prioritize objectives in order to facilitate aid and relief to those affected people. This task is accomplish by identifying the needs and requirements of the survivors of these calamities using responses via twitter analysis, these needs and requirements are certain objectives such as provisioning of food, tents for people, etc., all of these objectives can be prioritize based on certain words used by the survivors and transforming into tokens. These token are called as lexical normalization. In this work we analyze the lexical normalization of data generated by twitter by applying various techniques and visualize the investigations as the techniques are applied to process raw data from Twitter.