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Predicting Locations in Tweets

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, as location prediction is a useful pre-processing step for location extraction, by defining a number of features to represent tweets and conducting 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.
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Submitted on : Tuesday, May 26, 2020 - 10:41:59 AM
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  • HAL Id : hal-02624131, version 1
  • OATAO : 22055

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Thi Bich Ngoc Hoang, Véronique Moriceau, Josiane Mothe. Predicting Locations in Tweets. CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2017, Budapest, Hungary. ⟨hal-02624131⟩

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