Litofcenko, Julia, Karner, Dominik, Maier, Florentine. 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), 227-237.

RIS

@ARTICLE{Litofcenko2020,
title = {Methods for Classifying Nonprofit Organizations According to their Field of Activity: A Report on Semi-automated Methods Based on Text},
author = {Julia Litofcenko and Dominik Karner and Florentine Maier},
year = {2020},
doi = {https://doi.org/10.1007/s11266-019-00181-w},
url = {https://link.springer.com/article/10.1007%2Fs11266-019-00181-w#article-info},
volume = {31},
number = {1},
language = {EN},
pages = {227-237},
journal = {Voluntas: International Journal of Voluntary and Nonprofit Organizations},
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.},
}