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dc.contributor.authorMallela, Deepa
dc.contributor.authorAhlers, Dirk
dc.contributor.authorPera, Maria Soledad
dc.date.accessioned2018-02-09T14:12:00Z
dc.date.available2018-02-09T14:12:00Z
dc.date.created2017-08-29T14:37:48Z
dc.date.issued2017
dc.identifier.isbn978-1-4503-4951-2
dc.identifier.urihttp://hdl.handle.net/11250/2483788
dc.description.abstractWe present CEST, a generic method for detection and rich summarization of events occurring in a city. CEST exploits Twitter metadata, does not need prior information on events, and is event category and structure agnostic. We developed CEST to process unstructured documents and take advantage of shorthand notations, hashtags, keywords, geographical and temporal data, as well as sentiment within tweets to both detect and summarize arbitrary events without prior knowledge. We also introduce a novel strategy that analyzes sentiment and tweeting behavior over time to create a qualitative score that captures events’ overall appeal to attendees.nb_NO
dc.language.isoengnb_NO
dc.publisherAssociation for Computing Machinery (ACM)nb_NO
dc.relation.ispartofIEEE/WIC/ACM International Conference on Web Intelligence (WI)
dc.titleMining Twitter Features for Event Summarization and Ratingnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.identifier.cristin1489549
dc.description.localcode© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions of Computing Education, https://dl.acm.org/citation.cfm?doid=3106426.3106487nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknikk og informasjonsvitenskap
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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