Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9103
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dc.contributor.authorZengin M.S.-
dc.contributor.authorArslan R.-
dc.contributor.authorAkgun M.B.-
dc.date.accessioned2022-11-30T19:27:47Z-
dc.date.available2022-11-30T19:27:47Z-
dc.date.issued2022-
dc.identifier.isbn9.78167E+12-
dc.identifier.urihttps://doi.org/10.1109/SIU55565.2022.9864702-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/9103-
dc.description30th Signal Processing and Communications Applications Conference, SIU 2022 -- 15 May 2022 through 18 May 2022 -- -- 182415en_US
dc.description.abstractThe ever-increasing frequency of sharing on social media makes these platforms one of the primary sources of data for computational social science studies. Similarly, examining and analyzing large scale social media data-sets is crucial for governments as well as companies. However, as the amount of data increases, insights that need to be derived from the data using artificial intelligence based models becomes more and more demanding in terms of processing power. In fact, hardware requirements might dramatically increase if the insights are needed under real-time or near-real time constraints. In this study, we developed a distributed sentiment analysis model that utilizes a large social media data-set. 16 million tweets have been collected and grouped by the originating city. The sentiment analysis model was produced by fine-tuning the pre-trained BERT model. Distributed big data analytics engine, Apache Spark, is used to execute the trained model in a distributed fashion. For evaluation purposes, the prediction time on a single compute unit is compared with the distributed prediction time. Sentiment analysis model has been executed separately for each of the data-groups corresponding to 81 provinces. The data-set containing 16 million tweets used in this study, the Turkish sentiment analysis model produced, the distributed prediction code developed for Apache Spark and all the results of the study can be accessed from the address https://distributed-sentiment-analysis.github.io/. © 2022 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2022 30th Signal Processing and Communications Applications Conference, SIU 2022en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBERTen_US
dc.subjectBig dataen_US
dc.subjectdistributed data processingen_US
dc.subjectsentiment analysisen_US
dc.subjectBig dataen_US
dc.subjectData Analyticsen_US
dc.subjectData handlingen_US
dc.subjectForecastingen_US
dc.subjectSocial networking (online)en_US
dc.subjectAnalysis modelsen_US
dc.subjectBERTen_US
dc.subjectComputational social scienceen_US
dc.subjectData seten_US
dc.subjectDistributed data processingen_US
dc.subjectPrediction timeen_US
dc.subjectPrimary sourcesen_US
dc.subjectSentiment analysisen_US
dc.subjectSocial mediaen_US
dc.subjectSocial media datumen_US
dc.subjectSentiment analysisen_US
dc.titleDistributed Sentiment Analysis for Geo-Tagged Twitter Dataen_US
dc.title.alternativeCo?rafi Etiketli Twitter Verileri için Da?itik Duygu Analizien_US
dc.typeConference Objecten_US
dc.identifier.scopus2-s2.0-85138684842en_US
dc.identifier.doi10.1109/SIU55565.2022.9864702-
dc.authorscopusid57226399864-
dc.authorscopusid57226405651-
dc.authorscopusid36936068600-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.ozel2022v3_Editen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1tr-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Öğrenci Yayınları / Students' Publications
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