Automated Coding Using Machine Learning and Remapping the US nonprofit sector

dc.creatorMa, Ji
dc.date.accessioned2020-12-19T00:53:33Z
dc.date.available2020-12-19T00:53:33Z
dc.date.issued2020
dc.description.departmentLBJ School of Public Affairsen_US
dc.identifier.urihttps://hdl.handle.net/2152/83978
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/10971
dc.relation.ispartofPlanet Texas 2050- Published Researchen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.restrictionOpenen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectnon profit organizationen_US
dc.subjectneural networken_US
dc.subjectmachine learningen_US
dc.subjectcomputational social scienceen_US
dc.subjectNational Taxonomy of Exempt Entitiesen_US
dc.subjectBERTen_US
dc.titleAutomated Coding Using Machine Learning and Remapping the US nonprofit sectoren_US
dc.typePre-printen_US

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This is a research article that details a new machine learning classifier that reliably automates the coding process for U.S. nonprofits. The results show a richer understanding of nonprofit activities and the methodology could be applied to various Big Data analyses.

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