  In this paper, we present a tool for analyzing spatio-temporal distribution
of social anxiety. Twitter, one of the most popular social network services,
has been chosen as data source for analysis of social anxiety. Tweets (posted
on the Twitter) contain various emotions and thus these individual emotions
reflect social atmosphere and public opinion, which are often dependent on
spatial and temporal factors. The reason why we choose anxiety among various
emotions is that anxiety is very important emotion that is useful for observing
and understanding social events of communities. We develop a machine learning
based tool to analyze the changes of social atmosphere spatially and
temporally. Our tool classifies whether each Tweet contains anxious content or
not, and also estimates degree of Tweet anxiety. Furthermore, it also
visualizes spatio-temporal distribution of anxiety as a form of web
application, which is incorporated with physical map, word cloud, search engine
and chart viewer. Our tool is applied to a big tweet data in South Korea to
illustrate its usefulness for exploring social atmosphere and public opinion
spatio-temporally.
