Fine-grained Geolocation Prediction of Tweets with Human Machine Collaboration
This work addresses the need for accurate geolocation prediction in tweets to enable domain-specific analyses, such as for restaurant owners or policy planners, but it appears incremental as it builds on existing NLP and neural network methods.
The paper tackled the problem of predicting geolocation for non-geotagged tweets, which are critical for analyzing location-based opinions, by using deep neural networks and human-machine collaboration to achieve predictions at various granularities like neighborhood and zipcode, showing promising results.
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.