Publication:
Sentiment Analysis Using Transformers and Machine Learning Models

dc.authorscopusid57479138000
dc.authorscopusid22953804000
dc.contributor.authorIlgün, H.
dc.contributor.authorKilic, E.
dc.date.accessioned2025-12-11T00:28:09Z
dc.date.issued2021
dc.departmentOndokuz Mayıs Üniversitesien_US
dc.department-temp[Ilgün] Hüseyin, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkey; [Kilic] Erdal, Department of Computer Engineering, Ondokuz Mayis Üniversitesi, Samsun, Turkeyen_US
dc.description.abstractSocial media is an online network environment where internet users express their positive or negative opinions about any subject, business, product, or situation. It is an environment where users can view and share relevant content, articles, news, thoughts, daily events and all kinds of visual and audio materials thanks to fast access. Social media sentiment analysis is a popular field for various industries and academic studies. These studies, also known as idea mining, are carried out to classify the general feeling in a text. Studies have been conducted on this subject with machine learning models in big data and natural language processing. In our research, sentiment analysis will be shown on text data sets with machine learning models and transformers. © 2021 IEEEen_US
dc.identifier.doi10.1109/UBMK52708.2021.9558931
dc.identifier.endpage45en_US
dc.identifier.isbn9781665429085
dc.identifier.scopus2-s2.0-85125850364
dc.identifier.startpage42en_US
dc.identifier.urihttps://doi.org/10.1109/UBMK52708.2021.9558931
dc.identifier.urihttps://hdl.handle.net/20.500.12712/36486
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof-- 6th International Conference on Computer Science and Engineering, UBMK 2021 -- 2021-09-15 through 2021-09-17 -- Ankara -- 176826en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLogistic Regressionen_US
dc.subjectMachine Learningen_US
dc.subjectSupport Vector Machinesen_US
dc.subjectTransformersen_US
dc.titleSentiment Analysis Using Transformers and Machine Learning Modelsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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