Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features

Lianhua Chi, Kwan Hui Lim, Nebula Alam, Christopher J. Butler


Abstract
Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task “Geolocation Prediction in Twitter” of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, #hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, #hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance.
Anthology ID:
W16-3930
Volume:
Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
Month:
December
Year:
2016
Address:
Osaka, Japan
Editors:
Bo Han, Alan Ritter, Leon Derczynski, Wei Xu, Tim Baldwin
Venue:
WNUT
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
227–234
Language:
URL:
https://aclanthology.org/W16-3930
DOI:
Bibkey:
Cite (ACL):
Lianhua Chi, Kwan Hui Lim, Nebula Alam, and Christopher J. Butler. 2016. Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 227–234, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features (Chi et al., WNUT 2016)
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PDF:
https://aclanthology.org/W16-3930.pdf