Emotional community detection in social networks. (January 2018)

• Record Type: Journal Article
• Authors: Kanavos, Andreas; Perikos, Isidoros; Hatzilygeroudis, Ioannis; Tsakalidis, Athanasios
• Is Part Of: Computers & electrical engineering. Volume 65(2018)
• Page Start: 449
• Page End: 460
• Publication Date: 2018-01
• Subjects: Community analysis -- Graph mining -- Influential community detection -- Sentiment analysis -- Tweet emotion recognition -- User influence; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.09.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: It is vastly acknowledged that analyzing social networks is a very challenging research area. Take as a striking example the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. This comprises a fundamental aspect, which concerns the detection of user communities. In certain fields such as sociology and computer science where interactions and associations are often represented in the form of graphs, detecting communities is of vital importance. This paper addresses the need for an efficient and innovative methodology for community detection that will also leverage users' behavior on emotional level. Ekman emotional scale is the key point with which the methodology analyzes user's tweets in order to determine their emotional behavior. Consequently, the derived communities are estimated with the use of three different metrics, while the weighted version of a modularity community detection algorithm is utilized. There is substantial evidence indicating that our proposed methodology creates influential enough communities.