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

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.

Keywords: unsupervised learning, discovery, content analysis