Location Classification Based on Tweets

Abstract

Location classification is used for associating type to locations, to enrich maps and support a plethora of geospatial applications that rely on location types. Classification can be performed by humans, but using machine learning is more efficient and faster to react to changes than human-based classification. Machine learning can be used in lieu of human classification or for supporting it. In this paper we study the use of machine learning for Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts, e.g., tweets. Our goal is to correctly associate a set of tweets posted in a small radius around a given location with the corresponding location type, e.g., school, church, restaurant or museum. We explore two approaches to the problem: (a) a pipeline approach, where each post is first classified, and then the location associated with the set of posts is inferred from the individual post labels; and (b) a joint approach where the individual posts are simultaneously processed to yield the desired location type. We tested the two approaches over a data set of geotagged tweets. Our results demonstrate the superiority of the joint approach. Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, linear classifiers outperform deep neural network alternatives.