Thanks to the web-related and other advanced technologies, textual information is increasingly being stored in digital form and posted online. Automatic methods to analyze such textual information are becoming inevitable. Many of those methods are based on quantitative text features. Analysts face the challenge to choose the most appropriate features for their tasks. This requires effective approaches for evaluation and feature-engineering. In this paper we suggest an approach to visually evaluate text-analysis features as part of an interactive feedback loop between evaluation and feature engineering. We apply document-fingerprinting for visualizing text features as an integral part of the analytic process. Consequently, analysts are able to access interim results of the applied automatic methods and alter their properties to achieve better results. We implement and evaluate the methodology on two different tasks, namely opinion analysis and document summarization and show that our iterative method leads to improved performance.