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

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 Aust...

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Bibliographic Details
Published in:Voluntas (Manchester, England) England), 2020-02, Vol.31 (1), p.227-237
Main Authors: Litofcenko, Julia, Karner, Dominik, Maier, Florentine
Format: Article
Language:English
Subjects:
Automation
Classification
Data
Methodological problems
Nonprofit organizations
ORIGINAL PAPER
Political Science
Social Policy
Social Sciences
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Summary: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.
ISSN:0957-8765
1573-7888
DOI:10.1007/s11266-019-00181-w