Point-of-Interest Type Inference from Social Media Text

Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 804–810 December 4 - 7, 2020. c⃝2020 Association for Computational Linguistics 804 Point-of-Interest Type Inference from Social Media Text Danae S´anchez Villegasα Daniel Preot¸iuc-Pietroβ Nikolaos Aletrasα α Computer Science Department, University of Sheffield, UK β Bloomberg {dsanchezvillegas1, n.aletras}@sheffield.ac.uk dpreotiucpie@bloomberg.net Abstract Physical places help shape how we perceive the experiences we have there. 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 ∼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 ser- vices and cultural geography.1 1 Introduction Social networks such as Twitter allow users to share information about different aspects of their lives including feelings and experiences from places that they visit, from local restaurants to sport stadiums and parks. Feelings and emotions triggered by per- forming an activity or living an experience in a Point-of-Interest (POI) can give a glimpse of the atmosphere in that place (Tanasescu et al., 2013). In particular, the language used in posts from POIs is an important component that contributes to- ward the place’s identity and has been extensively studied in the context of social and cultural ge- ography (Tuan, 1991; Scollon and Scollon, 2003; Benwell and Stokoe, 2006). Social media posts from a particular location are usually focused on the person posting the content, rather than on pro- viding explicit information about the place. Table 1 displays example Twitter posts from different POIs. Users express their feelings related to a certain 1Data is available here: https://archive.org/ details/poi-data place (‘this places gives me war flashbacks’), com- ments and thoughts associated with the place they are in (‘few of us dressed appropriately’) or activi- ties they are performing (‘leaving the news station’, ‘on the way to the APCE Annual’). In this paper, we aim to study the language that people on Twitter use to share information about a specific place they are visiting. Thus, we define the prediction of a POI type given a post (i.e. tweet) as a multi-class classification task using only in- formation available at posting time. Given the text from a user’s post, our goal is to predict the correct type of the location it was posted, e.g. park, bar or shop. Inferring the type of place from a user’s post using linguistic information, is useful for cul- tural geographers to study a place’s identity (Tuan, 1991) and has downstream geosocial applications such as POI visualisation (McKenzie et al., …

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