Complexity of event data in texts makes it difficult to assess its content, especially when considering larger collections in which different sources report on the same or similar situations. We present a system that makes it possible to visually analyze complex event and emotion data extracted from texts. We show that we can abstract from different data models for events and emotions to a single data model that can show the complex relations in four dimensions. The visualization has been applied to analyze 1) dynamic developments in how people both conceive and express emotions in theater plays and 2) how stories are told from the perspectyive of their sources based on rich event data extracted from news or biographies.