Point-of-Interest Type Inference from Social Media Text
This work addresses the need for semantic place information in applications like recommendation systems and cultural geography, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of inferring point-of-interest types from social media text by introducing a dataset of ~200,000 English tweets from 2,761 locations and training classifiers that achieve a macro F1 score of 43.67 across eight classes.
Physical places help shape how we perceive the experiences we have there. For the first time, we study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of $\sim$200,000 English tweets published from 2,761 different points-of-interest in the U.S., enriched with place type information. We train classifiers to predict the type of the location a tweet was sent from that reach a macro F1 of 43.67 across eight classes and uncover the linguistic markers associated with each type of place. The ability to predict semantic place information from a tweet has applications in recommendation systems, personalization services and cultural geography.