Point-of-Interest Type Inference from Social Media Text
Danae Sánchez Villegas, Daniel Preotiuc-Pietro, Nikolaos Aletras
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 services and cultural geography.- Anthology ID:
- 2020.aacl-main.80
- Volume:
- 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
- Month:
- December
- Year:
- 2020
- Address:
- Suzhou, China
- Editors:
- Kam-Fai Wong, Kevin Knight, Hua Wu
- Venue:
- AACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 804–810
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.aacl-main.80/
- DOI:
- 10.18653/v1/2020.aacl-main.80
- Cite (ACL):
- Danae Sánchez Villegas, Daniel Preotiuc-Pietro, and Nikolaos Aletras. 2020. Point-of-Interest Type Inference from Social Media Text. In 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, Suzhou, China. Association for Computational Linguistics.
- Cite (Informal):
- Point-of-Interest Type Inference from Social Media Text (Sánchez Villegas et al., AACL 2020)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2020.aacl-main.80.pdf