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Twitter is a useful resource to analyze peoples' opinions on various topics.Often these topics are correlated or associated with locations from where theseTweet posts are made. For example, restaurant owners may need to know wheretheir target customers eat with respect to the sentiment of the posts maderelated to food, policy planners may need to analyze citizens' opinion onrelevant issues such as crime, safety, congestion, etc. with respect tospecific parts of the city, or county or state. As promising as this is, lessthan $1\%$ of the crawled Tweet posts come with geolocation tags. That makesaccurate prediction of Tweet posts for the non geo-tagged tweets very criticalto analyze data in various domains. In this research, we utilized millions ofTwitter posts and end-users domain expertise to build a set of deep neuralnetwork models using natural language processing (NLP) techniques, thatpredicts the geolocation of non geo-tagged Tweet posts at various level ofgranularities such as neighborhood, zipcode, and longitude with latitudes. Withmultiple neural architecture experiments, and a collaborative human-machineworkflow design, our ongoing work on geolocation detection shows promisingresults that empower end-users to correlate relationship between variables ofchoice with the location information.

Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration