--- date: '2022-11-27T13:14:43' hypothesis-meta: created: '2022-11-27T13:14:43.604240+00:00' document: title: - Analysis_of_REF_impact.pdf flagged: false group: __world__ hidden: false id: dTjBdG5VEe2sq0cgSuwbjw links: html: https://hypothes.is/a/dTjBdG5VEe2sq0cgSuwbjw incontext: https://hyp.is/dTjBdG5VEe2sq0cgSuwbjw/webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf json: https://hypothes.is/api/annotations/dTjBdG5VEe2sq0cgSuwbjw permissions: admin: - acct:ravenscroftj@hypothes.is delete: - acct:ravenscroftj@hypothes.is read: - group:__world__ update: - acct:ravenscroftj@hypothes.is tags: - lda - comprehensive impact target: - selector: - end: 39662 start: 39458 type: TextPositionSelector - exact: Topic modelling was used to determine common topics across the wholecorpus. Sixty-five topics were found (of which 60 were used) using theApache Mallet Toolkit Latent Dirichlet Allocation (LDA) algorithm. prefix: s to answer specific challenges. suffix: 12Topics are based on the freque type: TextQuoteSelector source: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf text: The authors used LDA with k=60 across full text case studies. The Apache Mallet implementation was used. updated: '2022-11-27T13:14:43.604240+00:00' uri: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf user: acct:ravenscroftj@hypothes.is user_info: display_name: James Ravenscroft in-reply-to: https://webarchive.nationalarchives.gov.uk/ukgwa/20170712131025mp_/http://www.hefce.ac.uk/media/HEFCE,2014/Content/Pubs/Independentresearch/2015/Analysis,of,REF,impact/Analysis_of_REF_impact.pdf tags: - lda - comprehensive impact - hypothesis type: annotation url: /annotations/2022/11/27/1669554883 ---
Topic modelling was used to determine common topics across the wholecorpus. Sixty-five topics were found (of which 60 were used) using theApache Mallet Toolkit Latent Dirichlet Allocation (LDA) algorithm.
The authors used LDA with k=60 across full text case studies. The Apache Mallet implementation was used.