ResearchPod Summary
Peer review is a critical but often opaque stage in scientific publishing. While previous research has analyzed review comments, most studies treat the review process as a static event rather than a dynamic, multi-round dialogue. This paper investigates how reviewers' focus and sentiment evolve across multiple rounds of revision for 11,063 accepted papers from Nature Communications.
The authors first segmented review comments into multiple rounds and identified fine-grained aspect clusters. They constructed a manually annotated corpus of approximately 5,000 sentences to train various deep learning models for aspect-based sentiment classification. The LCF-BERT-CDM model, which incorporates local context processing, achieved the highest performance with a Macro-F1 score of 82.65%. Using this model, the researchers analyzed the sentiment trajectories of specific aspects—such as experiments, research significance, and result analysis—across the review lifecycle.
The study reveals a clear temporal pattern: as papers move through successive rounds of review, the proportion of positive sentiment increases while negative sentiment decreases. This suggests that the revision process effectively addresses reviewer concerns, leading to a more favorable evaluation. Furthermore, the researchers found a negative correlation between sentiment scores for key aspects (particularly "experiments" and "result analysis") and the total number of review rounds. This implies that critical feedback on these specific aspects is a primary driver of longer review cycles. These findings provide actionable insights for authors, suggesting that focusing on these high-impact aspects during the initial drafting phase may help streamline the peer review process.
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