Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
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.
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