@inproceedings{mishra-2020-geolocation,
title = "Geolocation of Tweets with a {B}i{LSTM} Regression Model",
author = "Mishra, Piyush",
editor = {Zampieri, Marcos and
Nakov, Preslav and
Ljube{\v{s}}i{\'c}, Nikola and
Tiedemann, J{\"o}rg and
Scherrer, Yves},
booktitle = "Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics (ICCL)",
url = "https://aclanthology.org/2020.vardial-1.27/",
pages = "283--289",
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."
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%0 Conference Proceedings
%T Geolocation of Tweets with a BiLSTM Regression Model
%A Mishra, Piyush
%Y Zampieri, Marcos
%Y Nakov, Preslav
%Y Ljubešić, Nikola
%Y Tiedemann, Jörg
%Y Scherrer, Yves
%S Proceedings of the 7th Workshop on NLP for Similar Languages, Varieties and Dialects
%D 2020
%8 December
%I International Committee on Computational Linguistics (ICCL)
%C Barcelona, Spain (Online)
%F mishra-2020-geolocation
%X 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.
%U https://aclanthology.org/2020.vardial-1.27/
%P 283-289
Markdown (Informal)
[Geolocation of Tweets with a BiLSTM Regression Model](https://aclanthology.org/2020.vardial-1.27/) (Mishra, VarDial 2020)
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).