Potential Applications of Word Embedding Models to Sociological Theory: A Case Study of Twitter Data Analysis

Abstract

The aim of this study is to demonstrate the usefulness and applicability of the method using the word embedding model in vector space, which can realize the method examined in structuralist sociology (Bourdieu, etc.) at higher dimensions. The latter method refers to relational analysis that emphasizes the relationship of relative positions (distance) among actors within a social space. In this study, we collected Twitter data on "parental leave," created high-dimensional vector representation data, mapped it to a three-dimensional coordinate space, and conducted clustering to visualize the various practices of the actors in a certain degree from multiple perspectives, which are difficult to see from the public space.