Geolocation of Tweets with a BiLSTM Regression Model

Piyush Mishra


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)
Venues:
COLING | VarDial
SIG:
Publisher:
International Committee on Computational Linguistics (ICCL)
Note:
Pages:
283–289
Language:
URL:
https://aclanthology.org/2020.vardial-1.27
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/2020.vardial-1.27.pdf