File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1007/s11266-021-00399-7
- Scopus: eid_2-s2.0-85114671614
- WOS: WOS:000695467000007
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Identifying Nonprofits by Scaling Mission and Activity with Word Embedding
Title | Identifying Nonprofits by Scaling Mission and Activity with Word Embedding |
---|---|
Authors | |
Keywords | Word embedding Identification Document retrieval Nonprofit organizations Text-as-data |
Issue Date | 2021 |
Citation | Voluntas, 2021 How to Cite? |
Abstract | This study develops a new text-as-data method for organization identification, based on word embedding. We introduce and apply the method to identify identity-based nonprofit organizations, using the U.S. nonprofits’ mission and activity information reported in the IRS Form 990s in 2010–2016. Our results show that such method is simple but versatile. It complements the existing dictionary-based approaches and supervised machine learning methods for classification purposes and generates a reliable continuous measure of document-to-keyword relevance. Our approach provides a nonbinary alternative for nonprofit big data analyses. Using word embedding, researchers are able to identify organizations of interest, track possible changes over time and capture nonprofits’ multi-dimensionality. |
Persistent Identifier | http://hdl.handle.net/10722/307317 |
ISSN | 2023 Impact Factor: 2.3 2023 SCImago Journal Rankings: 0.901 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Haohan | - |
dc.contributor.author | Zhang, Ruodan | - |
dc.date.accessioned | 2021-11-03T06:22:22Z | - |
dc.date.available | 2021-11-03T06:22:22Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Voluntas, 2021 | - |
dc.identifier.issn | 0957-8765 | - |
dc.identifier.uri | http://hdl.handle.net/10722/307317 | - |
dc.description.abstract | This study develops a new text-as-data method for organization identification, based on word embedding. We introduce and apply the method to identify identity-based nonprofit organizations, using the U.S. nonprofits’ mission and activity information reported in the IRS Form 990s in 2010–2016. Our results show that such method is simple but versatile. It complements the existing dictionary-based approaches and supervised machine learning methods for classification purposes and generates a reliable continuous measure of document-to-keyword relevance. Our approach provides a nonbinary alternative for nonprofit big data analyses. Using word embedding, researchers are able to identify organizations of interest, track possible changes over time and capture nonprofits’ multi-dimensionality. | - |
dc.language | eng | - |
dc.relation.ispartof | Voluntas | - |
dc.subject | Word embedding | - |
dc.subject | Identification | - |
dc.subject | Document retrieval | - |
dc.subject | Nonprofit organizations | - |
dc.subject | Text-as-data | - |
dc.title | Identifying Nonprofits by Scaling Mission and Activity with Word Embedding | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s11266-021-00399-7 | - |
dc.identifier.scopus | eid_2-s2.0-85114671614 | - |
dc.identifier.isi | WOS:000695467000007 | - |