Community based emotional behaviour using ekman’s emotional scale

Abstract

In the current era, the analysis of social network data is one of the challenging tasks. Social networks are represented as a graphical structure where the users will be treated as nodes and the edges represent the social tie between the users. Research such as community identification, detection of centrality, detection of fraud, prediction of links and many other social issues are carried out in social network analysis. However, the community plays an important role in solving major issues in a real-world scenario. In a community structure, nodes inside a community are densely connected, whereas nodes between the communities are sparsely connected. Determining the emotional behavior from communities is the major concern because, emotional behavior of community helps us to solve problems like brand reachability, find target audience, build brand awareness and much more. Ekman’s emotional scale is a popular categorical model which assumes that there is a finite number of basic and discrete emotions and is used to classify the emotions. In this paper, a novel method is proposed to determine the community-specific emotional behavior of the users related to a particular topic. Communities are formed based on network topology rather than emotions. Girvan Newman algorithm is used to construct the communities of different users, who share their views on twitter media platforms regarding a topic. Then Ekman’s emotional scale is used to categorize the emotions of the users of each community. This identifies how the people of different communities react to an incident. The incident can be treated as NEWS/Trending threads on Twitter/Facebook shares. The emotional analysis is done community-specific so that the behavioral analysis of an incident is performed specifically for that community. Further, the comprehensive experimental analysis shows that the proposed methodology constructs influential communities and performs emotional analysis efficiently.