Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9913
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dc.contributor.authorHakdağlı, Özlem-
dc.contributor.authorÖzcan, Caner-
dc.contributor.authorOğul, İskender Ülgen-
dc.date.accessioned2021-01-24T18:31:40Z-
dc.date.available2021-01-24T18:31:40Z-
dc.date.issued2018-
dc.identifier.isbn978-1-5386-1501-0-
dc.identifier.issn2165-0608-
dc.identifier.urihttps://hdl.handle.net/11147/9913-
dc.description26th IEEE Signal Processing and Communications Applications Conference (SIU)en_US
dc.description.abstractWith today's developing technology, people's access to information and its production have reached a very fast level. These generated and obtained information are instantly created, entered into data systems and updated. Sources of streaming data can be transformed into valuable analysis results when they are handled with targeted methods. In this study, a text data field is determined to perform analysis on instantaneous generated data and Twitter, the richest platform for instant text data, is used. Twitter instantly generates a variety of data in large quantities and it presents it as open source using an API. A machine learning framework Apache Spark's stream analysis environment is used to analyze these resources. Situation analysis was performed using Support Vector Machine, Decision Trees and Logistic Regression algorithms presented under this environment. The results are presented in tables.en_US
dc.description.sponsorshipIEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Univen_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2018 26th Signal Processing and Communications Applications Conference, SIU 2018en_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference-
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectApache Sparken_US
dc.subjectSpark Streamingen_US
dc.subjectTwitteren_US
dc.subjectMachine Learningen_US
dc.subjectText Miningen_US
dc.titleStream text data analysis on twitter using apache spark streamingen_US
dc.typeConference Objecten_US
dc.institutionauthorOğul, İskender Ülgen-
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.wosWOS:000511448500393en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.languageiso639-1tr-
item.cerifentitytypePublications-
item.grantfulltextnone-
crisitem.author.dept01. Izmir Institute of Technology-
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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