Sentiment Analysis of Posts of Social Media Users in Their Socially Important Locations

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dc.contributor.authorEcemiş A.
dc.contributor.authorDokuz A.Ş.
dc.contributor.authorÇelik M.
dc.date.accessioned2019-08-01T13:38:39Z
dc.date.available2019-08-01T13:38:39Z
dc.date.issued2019
dc.departmentNiğde ÖHÜ
dc.description2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 -- 28 September 2018 through 30 September 2018 -- -- 144523
dc.description.abstractSocially important locations are frequently visited locations of social media users in their social media lifetime. Socially important locations reveal spatial preferences of social media users in a social media user group. Discovering socially important locations is important for recommending locations, marketing, urban planning, etc. However, the motivation of preferring these locations is unclear. This study is performed to reveal the motivation of location preferences of social media users. Polarity of socially important locations of social media users are detected using two popular document classification methods namely, SVM and Naïve Bayes. The results showed that SVM algorithm is more efficient than Naïve Bayes algorithm on detecting tweet polarities. Also, the location preference motivations could be revealed based on dominant polarity ratio. © 2018 IEEE.
dc.identifier.doi10.1109/IDAP.2018.8620832
dc.identifier.isbn9.78154E+12
dc.identifier.scopus2-s2.0-85062537176
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1109/IDAP.2018.8620832
dc.identifier.urihttps://hdl.handle.net/11480/1521
dc.identifier.wosWOS:000458717400110
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[0-Belirlenecek]
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectNaïve Bayes
dc.subjectpolarity detection
dc.subjectSocial media mining
dc.subjectsocially important locations
dc.subjectSVM
dc.subjectTwitter
dc.titleSentiment Analysis of Posts of Social Media Users in Their Socially Important Locations
dc.typeConference Object

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