Recent methodological work at the intersection of culture, cognition, and computational methods has drawn attention to how cultural schemas can be 'recovered' from social survey data. Defining cultural schemas as slowly learned, implicit, and unevenly distributed relational memory structures, researchers show how schemas—or rather, the downstream consequences of people drawing upon them—can be operationalized and measured from domain-specific survey modules. Respondents can then be sorted into 'classes' on the basis of the schema to which their survey response patterns best align. In this article, we extend this 'schematic class analysis' method to text data. We introduce concept class analysis (CoCA): a hybrid model that combines word embeddings and correlational class analysis to group documents across a corpus by the similarity of schemas recovered from them. We introduce the CoCA model, illustrate its validity and utility using simulations, and conclude with considerations for future research and applications.

中文翻译：

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概念类分析：一种识别文本中文化图式的方法

最近在文化、认知和计算方法的交叉点上的方法论工作引起了人们对如何从社会调查数据中“恢复”文化模式的关注。研究人员将文化图式定义为学习缓慢、隐含且分布不均的关系记忆结构，展示了图式（或者更确切地说，人们利用它们的下游后果）如何可以从特定领域的调查模块中操作和衡量。然后可以根据他们的调查响应模式最符合的模式将受访者分类为“类”。在本文中，我们将这种“图解类分析”方法扩展到文本数据。我们引入概念类分析（CoCA）：一种混合模型，它结合了词嵌入和相关类分析，通过从它们中恢复的模式的相似性来对整个语料库中的文档进行分组。我们介绍了 CoCA 模型，使用模拟说明了它的有效性和实用性，并总结了对未来研究和应用的考虑。