A Simple Scalable Neural Networks based Model for Geolocation Prediction in Twitter

Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma


Abstract
This paper describes a model that we submitted to W-NUT 2016 Shared task #1: Geolocation Prediction in Twitter. Our model classifies a tweet or a user to a city using a simple neural networks structure with fully-connected layers and average pooling processes. From the findings of previous geolocation prediction approaches, we integrated various user metadata along with message texts and trained the model with them. In the test run of the task, the model achieved the accuracy of 40.91% and the median distance error of 69.50 km in message-level prediction and the accuracy of 47.55% and the median distance error of 16.13 km in user-level prediction. These results are moderate performances in terms of accuracy and best performances in terms of distance. The results show a promising extension of neural networks based models for geolocation prediction where recent advances in neural networks can be added to enhance our current simple model.
Anthology ID:
W16-3931
Volume:
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
WNUT | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
235–239
Language:
URL:
https://www.aclweb.org/anthology/W16-3931
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-3931.pdf