The semantic mapping of words and co-words in contexts

TL;DR

This paper discusses how semantic mapping of words and co-words in contexts can be enhanced through statistical and computational techniques, making visualization of meaning relationships more accessible.

Contribution

It introduces methods for semantic mapping using correlation and factor analysis, providing practical guidance and software pointers for analysts.

Findings

Enhanced visualization of semantic relationships

Integration of correlation and factor analysis techniques

Practical tools for semantic mapping

Abstract

Meaning can be generated when information is related at a systemic level. Such a system can be an observer, but also a discourse, for example, operationalized as a set of documents. The measurement of semantics as similarity in patterns (correlations) and latent variables (factor analysis) has been enhanced by computer techniques and the use of statistics; for example, in "Latent Semantic Analysis". This communication provides an introduction, an example, pointers to relevant software, and summarizes the choices that can be made by the analyst. Visualization ("semantic mapping") is thus made more accessible.