Hoang, Thi Bich Ngoc and Moriceau, Véronique and Mothe, Josiane
Can we Predict Locations in Tweets? A Machine Learning Approach.
(2018)
International Journal of Computational Linguistics and Applications, 9. ISSN 0976-0962
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(Document in English)
PDF (Author's version) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader 176kB |
Abstract
Five hundred millions of tweets are posted daily, making Twitter a major social media from which topical information on events can be extracted. Events are represented by time, location and entity-related information. This paper focuses on location which is an important clue for both users and geo-spatial applications. We address the problem of predicting whether a tweet contains a location or not. Location prediction is a useful preprocessing step for location extraction. We defined a number of features to represent tweets and conducted intensive evaluation of machine learning parameters. We found that: (1) not only words appearing in a geography gazetteer are important but the occurrence of a preposition right before a proper noun also is. (2) it is possible to improve precision on location extraction if the occurrence of a location is predicted.
Item Type: | Article |
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HAL Id: | hal-02901421 |
Audience (journal): | International peer-reviewed journal |
Uncontrolled Keywords: | |
Institution: | French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE) Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE) Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE) Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE) Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE) Other partners > University of Economics – The University of Danang (VIETNAM) |
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Statistics: | download |
Deposited On: | 17 Jul 2020 08:54 |
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