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Constituent Input on Regulatory Initiatives: A Machine-Learning Approach to Efficiently and Effectively Analyze Unstructured Data.

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Title: Constituent Input on Regulatory Initiatives: A Machine-Learning Approach to Efficiently and Effectively Analyze Unstructured Data.
Authors: Ferguson, Daniel P.1 (AUTHOR), Harris, M. Kathleen1 (AUTHOR), Williams, L. Tyler2 (AUTHOR)
Source: Journal of Information Systems. Fall2023, Vol. 37 Issue 3, p119-138. 20p. 1 Diagram, 5 Charts, 2 Graphs.
Database: Business Source Ultimate
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PubType: Academic Journal
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  Data: Constituent Input on Regulatory Initiatives: A Machine-Learning Approach to Efficiently and Effectively Analyze Unstructured Data.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Information+Systems%22">Journal of Information Systems</searchLink>. Fall2023, Vol. 37 Issue 3, p119-138. 20p. 1 Diagram, 5 Charts, 2 Graphs.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=173176738
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.2308/ISYS-2021-032
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 119
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      – TitleFull: Constituent Input on Regulatory Initiatives: A Machine-Learning Approach to Efficiently and Effectively Analyze Unstructured Data.
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            NameFull: Ferguson, Daniel P.
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            NameFull: Harris, M. Kathleen
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            NameFull: Williams, L. Tyler
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            – D: 01
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              Text: Fall2023
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              Y: 2023
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              Value: 37
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