Text-Based Clustering and Analysis of Intelligent Argumentation Data

Argumentation is a method by which stakeholders exchange their viewpoints and rationale in the form of arguments in an organized manner in order to conduct collaborative decision making. Many online systems have been implemented in order to provide geographically distributed stakeholders with a structured method of argumentation. However, as these systems collect large amounts of arguments; it can be difficult to readily assess the major concerns which drive the discussion. This work presents a method for clustering and classifying a set of arguments, collected through an online argumentation tool, in order to model major concerns in an argumentation process. These clusters are further analyzed to provide a qualitative understanding of the influence they have on the decision making process