Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research

Ramit Debnath (), Sarah Darby, Ronita Bardhan, Kamiar Mohaddes and Minna Sunikka-Blank
Additional contact information
Sarah Darby: University of Oxford
Ronita Bardhan: Department of Architecture, University of Cambridge
Minna Sunikka-Blank: Department of Architecture, University of Cambridge

No EPRG2019, Working Papers from Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge

Keywords: energy policy; narratives; topic modelling; computational social science; text analysis; methodological framework (search for similar items in EconPapers)
JEL-codes: Q40 Q48 R28 (search for similar items in EconPapers)
Date: 2020-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-isf
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.jbs.cam.ac.uk/wp-content/uploads/2023/12/eprg-wp2019.pdf (application/pdf)

Related works:
Working Paper: Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:enp:wpaper:eprg2019

Access Statistics for this paper

More papers in Working Papers from Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge Contact information at EDIRC.
Bibliographic data for series maintained by Ruth Newman ().