The Correspondence Analysis Platform for Uncovering Deep Structure in Data and Information

TL;DR

This paper introduces a versatile platform using Correspondence Analysis to uncover deep structures in data, track changes, and identify anomalies, with applications in narrative analysis and policy decision making.

Contribution

It presents a general method for Euclidean embedding of diverse information spaces and models ultrametric relationships considering data sequences.

Findings

Effective dimensionality reduction for policy analysis

Ultrametric modeling captures temporal data changes

Application to narrative and decision-making contexts

Abstract

We study two aspects of information semantics: (i) the collection of all relationships, (ii) tracking and spotting anomaly and change. The first is implemented by endowing all relevant information spaces with a Euclidean metric in a common projected space. The second is modelled by an induced ultrametric. A very general way to achieve a Euclidean embedding of different information spaces based on cross-tabulation counts (and from other input data formats) is provided by Correspondence Analysis. From there, the induced ultrametric that we are particularly interested in takes a sequential - e.g. temporal - ordering of the data into account. We employ such a perspective to look at narrative, "the flow of thought and the flow of language" (Chafe). In application to policy decision making, we show how we can focus analysis in a small number of dimensions.