WordSeer: a knowledge synthesis environment for textual data

Authors:
Aditi Muralidharan;Marti A. Hearst;Christopher Fan
Affiliations:
UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA;UC Berkeley, Berkeley, CA, USA
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

We describe WordSeer, a tool whose goal is to help scholars and analysts discover patterns and formulate and test hypotheses about the contents of text collections, midway between what humanities scholars call a traditional "close read'' and the new "distant read" or "culturomics" approach. To this end, WordSeer allows for highly flexible "slicing and dicing" (hence "sliding") across a text collection. The tool allows users to view text from different angles by selecting subsets of data, viewing those as visualizations, moving laterally to view other subsets of data, slicing into another view, expanding the viewed data by relaxing constraints, and so on. We illustrate the text sliding capabilities of the tool with examples from a case study in the field of humanities and social sciences -- an analysis of how U.S. perceptions of China and Japan changed over the last 30 years.