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

Litofcenko, Julia ORCID: https://orcid.org/0000-0002-7484-739X and Karner, Dominik and Maier, Florentine ORCID: https://orcid.org/0000-0002-4687-4905 (2020) Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 31 (1). pp. 227-237. ISSN 0957-8765

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Abstract

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.

Item Type: Article
Additional Information: Open access funding provided by Vienna University of Economics and Business (WU).
Version of the Document: Published
Depositing User: ePub Administrator
Date Deposited: 11 Feb 2020 11:41
Last Modified: 11 Feb 2020 11:44
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/93042/
URI: https://epub.wu.ac.at/id/eprint/7477

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