İş, HafzullahTuncer, Taner2019-03-142019-03-142018-12-28İş, 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/41853/4876112619-9483http://dergipark.gov.tr/download/article-file/611710https://hdl.handle.net/20.500.12402/1905The 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" methodeninfo:eu-repo/semantics/openAccessTwitterQuality Scaling CriteriaSemantic and Emoticons AnalysisInteraction AnalysisArticle221018