  Current visual text analysis approaches rely on sophisticated processing
pipelines. Each step of such a pipeline potentially amplifies any uncertainties
from the previous step. To ensure the comprehensibility and interoperability of
the results, it is of paramount importance to clearly communicate the
uncertainty not only of the output but also within the pipeline. In this paper,
we characterize the sources of uncertainty along the visual text analysis
pipeline. Within its three phases of labeling, modeling, and analysis, we
identify six sources, discuss the type of uncertainty they create, and how they
propagate.
