Title:      Quantitative Discovery from Qualitative Information:
A General-Purpose Document Clustering Methodology

Authors:    Gary King, Justin Grimmer

Entrydate:  2009-07-19 20:59:16

Keywords:   unsupervised learning, discovery, content analysis

Abstract:   Many people attempt to discover useful information
by reading large quantities of unstructured text, but because of
known human limitations even experts are ill-suited to succeed at
this task. This difficulty has inspired the creation of numerous
automated cluster analysis methods to aid discovery. We address
two problems that plague this literature. First, the optimal use
of any one of these methods requires that it be applied only to a
specific substantive area, but the best area for each method is
rarely discussed and usually unknowable ex ante. We tackle this
problem with mathematical, statistical, and visualization tools
that define a search space built from the solutions to all
previously proposed cluster analysis methods (and any
qualitative approaches one has time to include) and enable a
user to explore it and quickly identify useful information.
Second, in part because of the nature of unsupervised learning
problems, cluster analysis methods are not routinely evaluated
in ways that make them vulnerable to being proven suboptimal or
less than useful in specific data types. We therefore propose
new experimental designs for evaluating these methods. With such
evaluation designs, we demonstrate that our computer-assisted
approach facilitates more efficient and insightful discovery of
useful information than either expert human coders using
qualitative or quantitative approaches or existing automated
methods. We (will) make available an easy-to-use software
package that implements all our suggestions.

http://polmeth.wustl.edu/retrieve.php?id=919

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