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dc.contributor.authorBifet, Alberten_NZ
dc.contributor.authorHolmes, Geoffreyen_NZ
dc.contributor.authorPfahringer, Bernharden_NZ
dc.contributor.authorGavaldà, Ricarden_NZ
dc.contributor.editorDiethe, Tomen_NZ
dc.contributor.editorBalcázar, José L.en_NZ
dc.contributor.editorShawe-Taylor, Johnen_NZ
dc.contributor.editorTȋrnăucă, Cristinaen_NZ
dc.coverage.spatialCastro Urdiales, Spainen_NZ
dc.date.accessioned2017-07-26T03:06:53Z
dc.date.available2011-10-19en_NZ
dc.date.available2017-07-26T03:06:53Z
dc.date.issued2011en_NZ
dc.identifier.citationBifet, A., Holmes, G., Pfahringer, B., & Gavaldà, R. (2011). Detecting sentiment change in Twitter streaming data. In T. Diethe, J. L. Balcázar, J. Shawe-Taylor, & C. Tȋrnăucă (Eds.), Proceedings of 2nd Workshop on Applications of Pattern Analysis (pp. 5–11). Castro Urdiales, Spain: JMLR.en
dc.identifier.urihttps://hdl.handle.net/10289/11228
dc.description.abstractMOA-TweetReader is a real-time system to read tweets in real time, to detect changes, and to find the terms whose frequency changed. Twitter is a micro-blogging service built to discover what is happening at any moment in time, anywhere in the world. Twitter messages are short, and generated constantly, and well suited for knowledge discovery using data stream mining. MOA-TweetReader is a software extension to the MOA framework. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherJMLRen_NZ
dc.relation.urihttp://jmlr.csail.mit.edu/proceedings/papers/v17/en_NZ
dc.rights© 2011 A. Bifet, G. Holmes, B. Pfahringer & R. Gavaldà.
dc.subjectMachine learning
dc.titleDetecting sentiment change in Twitter streaming dataen_NZ
dc.typeConference Contribution
dc.relation.isPartOfProceedings of 2nd Workshop on Applications of Pattern Analysisen_NZ
pubs.begin-page5
pubs.elements-id21244
pubs.end-page11
pubs.finish-date2011-10-21en_NZ
pubs.publisher-urlhttp://jmlr.csail.mit.edu/proceedings/papers/v17/en_NZ
pubs.start-date2011-10-19en_NZ


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