研究人员开发了一种深度学习方法来分析多轮同行评审中的情感演变，这是一个先前未被充分探索的课题。通过对《Nature Communications》11,063篇论文的评审意见进行分段，并在手动标注的语料库上训练模型，他们识别出了方面级情感的趋势。LCF-BERT-CDM模型取得了82.65%的Macro-F1分数。研究结果表明，随着评审轮次的增加，积极情感上升，消极情感下降，而“实验”和“研究意义”等方面的与评审轮次数量的相关性更强。 AI
影响 提供了一种分析科学论述的新颖方法，有可能改进评审流程并增进对研究趋势的理解。
排序理由 该集群包含一篇详细介绍一种新深度学习方法及其研究结果的学术论文。
在 arXiv cs.IR (Information Retrieval) 阅读 →
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arXiv:2606.24188v1 Announce Type: new Abstract: Mining sentiment information from the textual content of peer review comments offers valuable insights into the scientific evaluation process. However, previous studies are often constrained by coarse-grained analysis and the lack o…
Mining sentiment information from the textual content of peer review comments offers valuable insights into the scientific evaluation process. However, previous studies are often constrained by coarse-grained analysis and the lack of differentiation across review rounds. Notably,…