Description

In this work a model is engineered to depict topic relationships as graphs between detected topics of different time windows. By varying and shifting the time span of consideration the relationships between topics can be mapped with a variable complexity including the topic frequencies. Topic life cycles as well as changes in thematic relationships and their evolution become perceptible. Topics found can be matched in structure as well as their temporal progression to existing events.