Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction

Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, Tomoko Ohkuma


Abstract
We propose a novel geolocation prediction model using a complex neural network. Geolocation prediction in social media has attracted many researchers to use information of various types. Our model unifies text, metadata, and user network representations with an attention mechanism to overcome previous ensemble approaches. In an evaluation using two open datasets, the proposed model exhibited a maximum 3.8% increase in accuracy and a maximum of 6.6% increase in accuracy@161 against previous models. We further analyzed several intermediate layers of our model, which revealed that their states capture some statistical characteristics of the datasets.
Anthology ID:
P17-1116
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Editors:
Regina Barzilay, Min-Yen Kan
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1260–1272
Language:
URL:
https://aclanthology.org/P17-1116
DOI:
10.18653/v1/P17-1116
Bibkey:
Cite (ACL):
Yasuhide Miura, Motoki Taniguchi, Tomoki Taniguchi, and Tomoko Ohkuma. 2017. Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1260–1272, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Unifying Text, Metadata, and User Network Representations with a Neural Network for Geolocation Prediction (Miura et al., ACL 2017)
Copy Citation:
PDF:
https://aclanthology.org/P17-1116.pdf
Presentation:
 P17-1116.Presentation.pdf
Video:
 https://aclanthology.org/P17-1116.mp4