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High‐level event identification in social media
Author(s) -
Dashdorj Zolzaya,
Altangerel Erdenebaatar
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4668
Subject(s) - latent dirichlet allocation , computer science , identification (biology) , social media , data science , event (particle physics) , task (project management) , world wide web , volunteered geographic information , topic model , information retrieval , botany , physics , management , quantum mechanics , economics , biology
Summary The increasing numbers of large data sets generated by information technologies provide a great opportunity to better understand emerging topics in human society. Retrieving real‐world events from such data, particularly free‐text data, is a complicated task in Natural Language Processing and Location‐based Social Networks. In this work, we propose a new approach, which recognizes geo‐referenced high‐level events/activities mentioned in web sources adopting open gazetteers: OpenStreetMap and Google Maps. Our approach demonstrated on sampled news articles identifies events associated with the relevant topics using a latent Dirichlet allocation. This research is an essential step towards recommendation systems, urban planning, and monitoring.

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