Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
We present a new algorithm for inferring the home location of Twitter users at different granularities, including city, state, time zone, or geographic region, using the content of users' tweets and their tweeting behavior. Unlike existing approaches, our algorithm uses an ensemble of statistical and heuristic classifiers to predict locations and makes use of a geographic gazetteer dictionary to identify place-name entities. We find that a hierarchical classification approach, where time zone, state, or geographic region is predicted first and city is predicted next, can improve prediction accuracy. We have also analyzed movement variations of Twitter users, built a classifier to predict whether a user was travelling in a certain period of time, and use that to further improve the location detection accuracy. Experimental evidence suggests that our algorithm works well in practice and outperforms the best existing algorithms for predicting the home location of Twitter users.
Saeel Sandeep Nachane, Ojas Gramopadhye, et al.
EMNLP 2024
Ken C.L. Wong, Satyananda Kashyap, et al.
Pattern Recognition Letters
Alain Vaucher, Philippe Schwaller, et al.
AMLD EPFL 2022
Hironori Takeuchi, Tetsuya Nasukawa, et al.
Transactions of the Japanese Society for Artificial Intelligence