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Project description

FeatureLens is an interface to explore and visualize features in collections of text documents. It allows the exploration of frequent patterns found in the text: e.g. frequently used words but also frequent patterns of ngrams which leads to the discovering of fuzzy repetition patterns. FeatureLens integrates the results of text-mining algorithms into a meaningful representation of a text collection. Features can be compared, and occurences of the patterns are shown in the text. To help users in finding interesting patterns FeatureLens highlights features that have specific patterns of use in the collection (increasing, decreasing, spike behavior etc.

Participants

Anthony Don, Post-doc fellow (visiting from Labri, University Bordeaux I, France)
Catherine Plaisant, PhD, Associate Research Scientist, HCIL
Ben Shneiderman, PhD, Professor, Computer Science

Elena Zheleva, Machon Gregory, Sureyya Tarkan (Graduate Students, Computer Science, students of Ben Shneiderman's Information Visualization class project who worked with Anthony on developping FeatureLens as part of their class project.

Tanya Clement (PhD Graduate Student, English Department)
Loretta Auvil and Amit Kumar (collaborators from NCSA, U. of Illinois)

We also appreciate the feedback and suggestions of Celeste Paul, Matt Kirschenbaum and others from the MONK project and MITH the Maryland Institute for Technology in the Humanities