Social scientists have recently started discussing the utilization of text-mining tools as being fruitful for scaling inductively grounded close reading. We aim to progress in this direction and provide a contemporary contribution to the literature. By focusing on map analysis, we demonstrate the potential of text-mining tools for text analysis that approaches inductive but still formal in-depth analysis. We propose that a combination of text-mining tools addressing different layers of meaning facilitates a closer analysis of the dynamics of manifest and latent meanings than is currently acknowledged. To illustrate our approach, we combine grammatical parsing and topic modeling to operationalize communication structures within sentences and the semantic surroundings of these communication structures. We use a reliable and downloadable software application to analyze the dynamic interlacement of two layers of meaning over time. We do so by analyzing 15,371 newspaper articles on corporate responsibility published in the United States from 1950 to 2013.

中文翻译：

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分析大数据中的含义：使用语法解析和主题建模进行地图分析

社会科学家最近开始讨论文本挖掘工具的使用，因为它对于扩展归纳接地的仔细阅读很有成效。我们的目标是朝着这个方向前进，并为文学做出当代贡献。通过专注于地图分析，我们展示了用于文本分析的文本挖掘工具的潜力，该工具接近归纳但仍然是正式的深入分析。我们建议结合文本挖掘工具来处理不同层次的含义，有助于对显性和潜在含义的动态进行比目前公认的更密切的分析。为了说明我们的方法，我们结合语法解析和主题建模来操作句子内的交流结构和这些交流结构的语义环境。我们使用可靠且可下载的软件应用程序来分析两层含义随时间的动态交错。为此，我们分析了 1950 年至 2013 年在美国发表的 15,371 篇关于企业责任的报纸文章。