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Deep learning tracks sentiment shifts in peer reviews over multiple rounds

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a deep learning approach to analyze sentiment evolution in multi-round peer reviews, a topic previously underexplored. By segmenting review comments from 11,063 papers in Nature Communications and training models on a manually annotated corpus, they identified trends in aspect-level sentiments. The LCF-BERT-CDM model achieved a Macro-F1 score of 82.65%. Findings indicate that as review rounds increase, positive sentiment rises while negative sentiment declines, with aspects like "experiments" and "research significance" showing stronger correlations with the number of rounds. AI

IMPACT Provides a novel method for analyzing scientific discourse, potentially improving review processes and understanding research trends.

RANK_REASON The cluster contains an academic paper detailing a new deep learning approach and its findings.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning tracks sentiment shifts in peer reviews over multiple rounds

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Ruxue Hana, Haomin Zhoua, Jiangtao Zhong, Chengzhi Zhang ·

    Aspect-Based Sentiment Evolution and its Correlation with Review Rounds in Multi-Round Peer Reviews: A Deep Learning Approach

    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…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Chengzhi Zhang ·

    Aspect-Based Sentiment Evolution and its Correlation with Review Rounds in Multi-Round Peer Reviews: A Deep Learning Approach

    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,…

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