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 of differentiation across review rounds. Notably, the dynamic shifts in reviewers' focus and sentiment tendencies throughout multiple review stages remain underexplored. To address this gap, the present study investigates the distribution and evolution of aspect-level sentiments and examines their correlation with the n
The proliferation of digital textual data in academic and professional peer review systems necessitates more sophisticated tools for analysis and process improvement.
This research provides a more granular understanding of sentiment dynamics within peer review, potentially leading to fairer and more efficient evaluation processes.
The ability to track aspect-level sentiment evolution could inform better review mechanisms and highlight reviewer biases or changing foci over time.
- · Academic researchers
- · Journal editors
- · Peer review platforms
- · AI/ML developers
- · Inefficient review processes
Improved understanding of reviewer behavior and sentiment progression in multi-round reviews.
Development of smart peer review systems that can detect potential biases or shifts in review focus automatically.
Enhanced overall quality and fairness of scientific and academic publishing through optimized review workflows.
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