Twitter users’ emotion, emoticons and scaling metrics based categoric interaction analysis

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dc.authorid0000-0002-1395-1767en_US
dc.contributor.authorİş, Hafzullah
dc.contributor.authorTuncer, Taner
dc.date.accessioned2019-03-14T07:23:57Z
dc.date.available2019-03-14T07:23:57Z
dc.date.issued2018-12-28
dc.departmentBatman Üniversitesi Rektörlüken_US
dc.description.abstractThe popularity and use of social networks has also begun to increase in parallel with the worldwide increasing accessibility and means of access to the Internet. As one of the world's most popular social networks, Twitter is a platform where users are interacting through follow-up, sharing, messaging and appreciation tools, sharing their ideas and emotions in a variety of individual and corporate contexts. Therefore, Twitter is intense, dynamic and always an up-to-date data source. Identifying and correlating the physical and emotional interaction of users can be valuable in political, social, academic and commercial aspects. Users' physical networking with each other and emotional analysis can be done with many tools and applications. The character, tendency and impact analysis of the users can be used in the development of business intelligence applications and in the determination of social strategies. In this study, a large Twitter user group is divided into four categories: political, Entertainment, Sports, Trade Marks. Then, the physical and emotional interaction of each category was revealed. The Physical interaction metrics determined as centrality, intensity, reciprocity and modularity while emotional interaction metrics were determined as resistance, passion, reach and emotionality. Positive, negative and neutral states of sharing were discussed in emotional measurement. Beside that, emoji-containing tweets have been transformed into texts and are especially included in emotion analysis. After all the metrics were calculated, physical and emotional interaction structures and overlap rates were revealed using "Interaction and Semantic Clustering Based Multinetwork Analysis" methoden_US
dc.identifier.citationİş, H , Tuncer, T . (2018). Twitter Users’ Emotion, Emoticons and Scaling Metrics Based Categoric Interaction Analysis. Journal of Engineering and Technology, 2 (2), 10-18. Retrieved from http://dergipark.gov.tr/jetech/issue/41853/487611en_US
dc.identifier.endpage18en_US
dc.identifier.issn2619-9483
dc.identifier.issue2en_US
dc.identifier.startpage10en_US
dc.identifier.urihttp://dergipark.gov.tr/download/article-file/611710
dc.identifier.urihttps://hdl.handle.net/20.500.12402/1905
dc.identifier.volume2en_US
dc.language.isoenen_US
dc.publisherBatman Üniversitesien_US
dc.relation.journalJournal of Engineering and Technologyen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Yayınıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectTwitteren_US
dc.subjectQuality Scaling Criteriaen_US
dc.subjectSemantic and Emoticons Analysisen_US
dc.subjectInteraction Analysisen_US
dc.titleTwitter users’ emotion, emoticons and scaling metrics based categoric interaction analysisen_US
dc.typeArticleen_US

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