1. Automatically detecting open academic review praise and criticism
- Author
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Eleanor-Rose Papas, Liz Allen, Zena Nyakoojo, Verena Weigert, and Mike Thelwall
- Subjects
Matching (statistics) ,business.industry ,media_common.quotation_subject ,05 social sciences ,Applied psychology ,Sentiment analysis ,02 engineering and technology ,Library and Information Sciences ,Lexicon ,Computer Science Applications ,Test (assessment) ,Publishing ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Criticism ,0509 other social sciences ,Praise ,050904 information & library sciences ,Psychology ,business ,Information Systems ,media_common - Abstract
PurposePeer 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/approachPeerJudge 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.FindingsPeerJudge 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/valuePeerJudge 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.
- Published
- 2020