Recently, social media, such as Twitter, has been success- fully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media dur- ing disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social me- dia streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualiza- tion of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geo- graphic visualization. In particular, we provide an entropy- based metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how vi- sual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.