Automatically detecting open academic review praise and criticism
Abstract
Purpose: Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research’s open peer review publishing platform. Design/methodology/approach: PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings. Findings: PeerJudge can predict F1000Research judgements from negative evaluations in reviewers’ comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision. Originality/value: PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments.Citation
Thelwall, M., Papas, E., Nyakoojo, Z., Allen, L. and Weigert, V. (2020) Automatically detecting open academic review praise and criticism, Online Information Review 44 (5), pp. 1057-1076. DOI: 10.1108/OIR-11-2019-0347Publisher
EmeraldJournal
Online Information ReviewType
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by Emerald in Online Information Review on 15 June 2020. The accepted version of the publication may differ from the final published version, accessible at https://doi.org/10.1108/OIR-11-2019-0347.ISSN
1468-4527ae974a485f413a2113503eed53cd6c53
10.1108/OIR-11-2019-0347
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/