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Identifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data

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dc.bibliographiccitation.firstpage33
dc.bibliographiccitation.lastpage49
dc.bibliographiccitation.seriesnr12887
dc.contributor.authorLuber, Mattias
dc.contributor.authorWeisser, Christoph
dc.contributor.authorSäfken, Benjamin
dc.contributor.authorSilbersdorff, Alexander
dc.contributor.authorKneib, Thomas
dc.contributor.authorKis-Katos, Krisztina
dc.contributor.editorBright, Jonathan
dc.contributor.editorGiachanou, Anastasia
dc.contributor.editorSpaiser, Viktoria
dc.contributor.editorSpezzano, Francesca
dc.contributor.editorGeorge, Anna
dc.contributor.editorPavliuc, Alexandra
dc.date.accessioned2023-10-06T22:50:56Z
dc.date.available2023-10-06T22:50:56Z
dc.date.issued2021
dc.identifier.doi10.1007/978-3-030-87031-7_3
dc.identifier.urihttps://resolver.sub.uni-goettingen.de/purl?gro-2/136616
dc.item.fulltextNo Fulltext
dc.notes.internDOI-Import WOS-2023-10-07
dc.publisherSpringer International Publishing
dc.publisher.placeCham
dc.relation.crisseriesLecture Notes in Computer Science
dc.relation.eisbn978-3-030-87031-7
dc.relation.isbn978-3-030-87030-0
dc.relation.ispartofDisinformation in Open Online Media : Third Multidisciplinary International Symposium, MISDOOM 2021, Virtual Event, September 21–22, 2021, Proceedings
dc.titleIdentifying Topical Shifts in Twitter Streams: An Integration of Non-negative Matrix Factorisation, Sentiment Analysis and Structural Break Models for Large Scale Data
dc.typebook_chapter
dc.type.internalPublicationyes
dspace.entity.typePublication

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