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 of these associations, then demonstrate applications of word embeddings for macro-cultural investigation with a longitudinal analysis of the coevolution of gender and class associations in the United States over the 20th century and a comparative analysis of historic distinctions between markers of gender and class in the U.S. and Britain. We argue that the success of these high-dimensional models motivates a move towards "high-dimensional theorizing" of meanings, identities and cultural processes.

本文介绍了一种新方法，神经网络词嵌入模型，以用于大规模文本分析，揭示了这些模型如何比以前的方法更能产生丰富的文化联想和类别，推进了一个与当代身份和文化理论一致的意义关系模型，以高维空间中的向量几何关系表示单词之间的语义关系。作者展示了词嵌入模型在宏观文化调查中的应用，并给出了对20世纪美国性别和阶级联系的纵向分析以及对美英性别和阶级标记历史区别的比较分析。作者认为，这些高维模型的成功促使朝着“高维度理论化”的意义、身份和文化过程的方向发展。

文化的几何学：通过词嵌入分析含义