  Appropriate evaluation and experimental design are fundamental for empirical
sciences, particularly in data-driven fields. Due to the successes in
computational modeling of languages, for instance, research outcomes are having
an increasingly immediate impact on end users. As the gap in adoption by end
users decreases, the need increases to ensure that tools and models developed
by the research communities and practitioners are reliable, trustworthy, and
supportive of the users in their goals. In this position paper, we focus on the
issues of evaluating visual text analytics approaches. We take an
interdisciplinary perspective from the visualization and natural language
processing communities, as we argue that the design and validation of visual
text analytics include concerns beyond computational or visual/interactive
methods on their own. We identify four key groups of challenges for evaluating
visual text analytics approaches (data ambiguity, experimental design, user
trust, and "big picture" concerns) and provide suggestions for research
opportunities from an interdisciplinary perspective.
