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Can we Predict Locations in Tweets? A Machine Learning Approach

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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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
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)
Laboratory name:
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Deposited On:17 Jul 2020 08:54

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