Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text

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There are various methods for classifying nonprofit organizations (NPOs) according to their field of activity. We report our experiences using two semi-automated methods based on textual data: rule-based classification and machine learning with curated keywords. We use those methods to classify Austrian nonprofit organizations based on the International Classification of Nonprofit Organizations. Those methods can provide a solution to the widespread research problem that quantitative data on the activities of NPOs are needed but not readily available from administrative data, long high-quality texts describing NPOs’ activities are mostly unavailable, and human labor resources are limited. We find that in such a setting, rule-based classification performs about as well as manual human coding in terms of precision and sensitivity, while being much more labor-saving. Hence, we share our insights on how to efficiently implement such a rule-based approach. To address scholars with a background in data analytics as well as those without, we provide non-technical explanations and open-source sample code that is free to use and adapt.
Original languageEnglish
Pages (from-to)227 - 237
JournalVoluntas: International Journal of Voluntary and Nonprofit Organizations
Issue number1
Publication statusPublished - 2020

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 506009 Organisation theory
  • 502023 NPO research
  • 509005 Gerontology

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