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The Effects of Corpus Type and Size on the Classification of News

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dc.contributor.authorCetin, Fauna Zehra
dc.contributor.authorKurttekin, Omer
dc.contributor.authorAmasyali, M. Fatih
dc.date.accessioned2026-06-27T14:27:45Z
dc.date.issued2019
dc.description.abstractA tremendous amount of data is produced and stored in every second. This increase causes problems in controlling, storing and analyzing the data. These data have to be classified in order to access information rapidly. We researched word embedding tools, the effects of corpus types and size on the success of classification. We present the large volume data set of Turkish news that will be classified, unlabeled 2 million and 20 million Turkish tweets to researchers. Word embedding tools GloVe and fastText success were inspected on 40 thousand Turkish news that were labeled 13 different classes, 2 millions of tweets and 20 millions of tweets.en
dc.identifier.isbn978-1-7281-1904-5
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/20.500.14981/60878
dc.identifier.wos000518994300010
dc.language.isotur
dc.publisherIEEE
dc.relation.conference27th Signal Processing and Communications Applications Conference (SIU)
dc.relation.ispartof2019 27TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU)
dc.subjectfastText
dc.subjectGloVe
dc.subjectNews Classification
dc.subjectText Classification
dc.subjectWord Embedding
dc.subjectNatural Language Processing
dc.subjectEngineering
dc.subjectTelecommunications
dc.titleThe Effects of Corpus Type and Size on the Classification of News
dc.typeProceedings Paper
dspace.entity.typePublication
local.import.sourceWOS

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