Multi-opinion Ring: visualizing and predicting multiple opinion orientations in online social media

Abstract

In the era of the Social Web, actors (e.g. people, organizations, nations, etc) of online social media often voice out their opinions towards a variety of opinion targets. Extracting and visualizing distributions of multiple opinions among actors facilitates individuals or organizations to extract valuable social intelligence from online social media. The main contribution of our research reported in this paper is the development of a novel opinion analysis methodology named Multi-opinion Ring for visualizing and predicting multiple opinion orientations held by different groups of actors in online social media. In particular, the proposed Multi-opinion Ring method combines visualization techniques with machine learning methods to predict the opinion inclinations of actors who are originally neutral to different opinion targets. A series of controlled experiments, user-based evaluations, and case studies show that the proposed Multi-opinion Ring method significantly outperforms classical visualization methods in terms of the cohesiveness of the graphical layout and the informativeness of the visualized contents.

Visualizing Group Structures in Graphs: A Survey

“…We identified 11 techniques that use only color to visualize group membership explicitly [Dek01, DS13, DYL*15, DYLL15, IMMS09, LWC*14, NIST12, SGKS15, SKL*14, TLTC05, vHW08]. Nodes that belong to only one group are simply colored with respect to that group [DS13, DYL*15, DYLL15, SGKS15, SKL*14, vHW08] (Figure a). For flat overlapping groups , nodes are represented using glyphs—‘graphical objects designed to convey multiple data values' [War04].…”