Abstract
Identifying a user’s location can be useful for recommendation systems, demographic analyses, and disaster outbreak monitoring. Although Twitter allows users to voluntarily reveal their location, such information isn’t universally available. Analyzing a tweet can provide a general estimation of a tweet location while giving insight into the dialect of the user and other linguistic markers. Such linguistic attributes can be used to provide a regional approximation of tweet origins. In this paper, we present a neural regression model that can identify the linguistic intricacies of a tweet to predict the location of the user. The final model identifies the dialect embedded in the tweet and predicts the location of the tweet.- Anthology ID:
- 2020.vardial-1.27
- Volume:
- Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects
- Month:
- December
- Year:
- 2020
- Address:
- Barcelona, Spain (Online)
- Editors:
- Marcos Zampieri, Preslav Nakov, Nikola Ljubešić, Jörg Tiedemann, Yves Scherrer
- Venue:
- VarDial
- SIG:
- Publisher:
- International Committee on Computational Linguistics (ICCL)
- Note:
- Pages:
- 283–289
- Language:
- URL:
- https://aclanthology.org/2020.vardial-1.27
- DOI:
- Cite (ACL):
- Piyush Mishra. 2020. Geolocation of Tweets with a BiLSTM Regression Model. In Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects, pages 283–289, Barcelona, Spain (Online). International Committee on Computational Linguistics (ICCL).
- Cite (Informal):
- Geolocation of Tweets with a BiLSTM Regression Model (Mishra, VarDial 2020)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2020.vardial-1.27.pdf