This study develops a new text-as-data method for organization identification, based on word embedding. We introduce and apply the method to identify identity-based nonprofit organizations, using the U.S. nonprofits’ mission and activity information reported in the IRS Form 990s in 2010–2016. Our results show that such method is simple but versatile. It complements the existing dictionary-based approaches and supervised machine learning methods for classification purposes and generates a reliable continuous measure of document-to-keyword relevance. Our approach provides a nonbinary alternative for nonprofit big data analyses. Using word embedding, researchers are able to identify organizations of interest, track possible changes over time and capture nonprofits’ multi-dimensionality.

本研究基于词嵌入开发了一种用于组织识别的新文本数据方法。我们介绍并应用该方法来识别基于身份的非营利组织，使用美国非营利组织在 2010-2016 年 IRS 990 表格中报告的使命和活动信息。我们的结果表明这种方法简单但通用。它补充了现有的基于字典的方法和用于分类目的的监督机器学习方法，并生成了文档到关键字相关性的可靠连续度量。我们的方法为非营利性大数据分析提供了一种非二进制替代方案。使用词嵌入，研究人员能够识别感兴趣的组织，跟踪随时间可能发生的变化并捕捉非营利组织的多维性。