# Aspect-based Sentiment Analysis of Scientific Reviews

arXiv: https://arxiv.org/abs/2006.03257 · HTML: https://mlsanity.com/paper/2006.03257/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 24 of 24)

- [ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing](https://mlsanity.com/paper/2306.00622/) (2023)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions](https://mlsanity.com/paper/2110.08520/) (2021)
- [Automated Focused Feedback Generation for Scientific Writing Assistance](https://mlsanity.com/paper/2405.20477/) (2024)
- [What Can Natural Language Processing Do for Peer Review?](https://mlsanity.com/paper/2405.06563/) (2024)
- [Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM Reviews](https://mlsanity.com/paper/2502.17086/) (2025)
- [Exploring Jiu-Jitsu Argumentation for Writing Peer Review Rebuttals](https://mlsanity.com/paper/2311.03998/) (2023)
- [Insights from the ICLR Peer Review and Rebuttal Process](https://mlsanity.com/paper/2511.15462/) (2025)
- [PeerArg: Argumentative Peer Review with LLMs](https://mlsanity.com/paper/2409.16813/) (2024)
- [COMPARE: A Taxonomy and Dataset of Comparison Discussions in Peer Reviews](https://mlsanity.com/paper/2108.04366/) (2021)
- [Identifying Aspects in Peer Reviews](https://mlsanity.com/paper/2504.06910/) (2025)
- [Decoding Demographic un-fairness from Indian Names](https://mlsanity.com/paper/2209.03089/) (2022)
- [Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction](https://mlsanity.com/paper/2209.10292/) (2022)
- [When Reviewers Lock Horn: Finding Disagreement in Scientific Peer Reviews](https://mlsanity.com/paper/2310.18685/) (2023)
- [Artificial intelligence technologies to support research assessment: A review](https://mlsanity.com/paper/2212.06574/) (2022)
- [Personality Detection and Analysis using Twitter Data](https://mlsanity.com/paper/2309.05497/) (2023)
- [Deep Transfer Learning Based Peer Review Aggregation and Meta-review Generation for Scientific Articles](https://mlsanity.com/paper/2410.04202/) (2024)
- [QuadAI at SemEval-2026 Task 3: Ensemble Learning of Hybrid RoBERTa and LLMs for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2603.07766/) (2026)
- [Decoupling Scores and Text: The Politeness Principle in Peer Review](https://mlsanity.com/paper/2604.14162/) (2026)
- [Which Review Aspect Has a Greater Impact on the Duration of Open Peer Review in Multiple Rounds? -- Evidence from Nature Communications](https://mlsanity.com/paper/2606.21904/) (2026)
- [Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications](https://mlsanity.com/paper/2303.13191/) (2023)
- [Target-Dependent Multimodal Sentiment Analysis Via Employing Visual-to Emotional-Caption Translation Network using Visual-Caption Pairs](https://mlsanity.com/paper/2408.10248/) (2024)
- [Aspect-Based Sentiment Evolution and its Correlation with Review Rounds in Multi-Round Peer Reviews: A Deep Learning Approach](https://mlsanity.com/paper/2606.24188/) (2026)

## Abstract (arXiv)

Scientific papers are complex and understanding the usefulness of these papers requires prior knowledge. Peer reviews are comments on a paper provided by designated experts on that field and hold a substantial amount of information, not only for the editors and chairs to make the final decision, but also to judge the potential impact of the paper. In this paper, we propose to use aspect-based sentiment analysis of scientific reviews to be able to extract useful information, which correlates well with the accept/reject decision. While working on a dataset of close to 8k reviews from ICLR, one of the top conferences in the field of machine learning, we use an active learning framework to build a training dataset for aspect prediction, which is further used to obtain the aspects and sentiments for the entire dataset. We show that the distribution of aspect-based sentiments obtained from a review is significantly different for accepted and rejected papers. We use the aspect sentiments from these reviews to make an intriguing observation, certain aspects present in a paper and discussed in the review strongly determine the final recommendation. As a second objective, we quantify the extent of disagreement among the reviewers refereeing a paper. We also investigate the extent of disagreement between the reviewers and the chair and find that the inter-reviewer disagreement may have a link to the disagreement with the chair. One of the most interesting observations from this study is that reviews, where the reviewer score and the aspect sentiments extracted from the review text written by the reviewer are consistent, are also more likely to be concurrent with the chair's decision.