Interdonato Roberto, Doucet Antoine, Guillaume Jean-Loup.
2018. Unsupervised Crisis Information Extraction from Twitter Data.
In : Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2018. IEEE, ACM
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Version publiée
- Anglais
Accès réservé aux personnels Cirad Utilisation soumise à autorisation de l'auteur ou du Cirad. RP-ASONAM_2018_paper_155.pdf Télécharger (112kB) | Demander une copie |
Résumé : While microblogging-based Online Social Networks have become an attractive data source in emergency situations, overcoming information overload is still not trivial. We propose a framework which integrates natural language processing and clustering techniques in order to produce a ranking of relevant tweets based on their informativeness. Experiments on four Twitter collections in two languages (English and French) proved the significance of our approach.
Mots-clés libres : Crisis management, Ranking function, Natural language processing
Auteurs et affiliations
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Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA)
ORCID: 0000-0002-0536-6277
- Doucet Antoine, Université de La Rochelle (FRA)
- Guillaume Jean-Loup, Université de La Rochelle (FRA)
Source : Cirad-Agritrop (https://agritrop.cirad.fr/590762/)
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