  Twitter is a useful resource to analyze peoples' opinions on various topics.
Often these topics are correlated or associated with locations from where these
Tweet posts are made. For example, restaurant owners may need to know where
their target customers eat with respect to the sentiment of the posts made
related to food, policy planners may need to analyze citizens' opinion on
relevant issues such as crime, safety, congestion, etc. with respect to
specific parts of the city, or county or state. As promising as this is, less
than $1\%$ of the crawled Tweet posts come with geolocation tags. That makes
accurate prediction of Tweet posts for the non geo-tagged tweets very critical
to analyze data in various domains. In this research, we utilized millions of
Twitter posts and end-users domain expertise to build a set of deep neural
network models using natural language processing (NLP) techniques, that
predicts the geolocation of non geo-tagged Tweet posts at various level of
granularities such as neighborhood, zipcode, and longitude with latitudes. With
multiple neural architecture experiments, and a collaborative human-machine
workflow design, our ongoing work on geolocation detection shows promising
results that empower end-users to correlate relationship between variables of
choice with the location information.
