TL;DR
This paper introduces a new method using neural network word embeddings to analyze cultural meanings and associations through geometric relationships in high-dimensional space, providing richer insights than previous approaches.
Contribution
It demonstrates how word embeddings can reveal cultural dimensions and associations, and applies this to longitudinal and cross-national cultural analyses, advancing cultural analysis methods.
Findings
Word differences correspond to cultural dimensions
Embeddings reflect shared cultural connotations
Applications include longitudinal and cross-national studies
Abstract
We demonstrate the utility of a new methodological tool, neural-network word embedding models, for large-scale text analysis, revealing how these models produce richer insights into cultural associations and categories than possible with prior methods. Word embeddings represent semantic relations between words as geometric relationships between vectors in a high-dimensional space, operationalizing a relational model of meaning consistent with contemporary theories of identity and culture. We show that dimensions induced by word differences (e.g. man - woman, rich - poor, black - white, liberal - conservative) in these vector spaces closely correspond to dimensions of cultural meaning, and the projection of words onto these dimensions reflects widely shared cultural connotations when compared to surveyed responses and labeled historical data. We pilot a method for testing the stability…
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