# EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

arXiv: https://arxiv.org/abs/2508.17008 · HTML: https://mlsanity.com/paper/2508.17008/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges](https://mlsanity.com/paper/2203.01054/) (2022)
- [Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training](https://mlsanity.com/paper/2111.02194/) (2021)
- [On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training](https://mlsanity.com/paper/2304.09563/) (2023)
- [A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends](https://mlsanity.com/paper/2311.10777/) (2023)
- [PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2208.01368/) (2022)
- [Survey of Aspect-based Sentiment Analysis Datasets](https://mlsanity.com/paper/2204.05232/) (2022)
- [iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples](https://mlsanity.com/paper/2311.03896/) (2023)

## Built on by (most cited first, 5 of 5)

- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- [Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2511.03034/) (2025)
- [ACAT: A Collaborative Platform for Efficient Aspect-Based Sentiment Dataset Annotation](https://mlsanity.com/paper/2606.04189/) (2026)
- [Shifting from Discrete to Continuous Reference Data: QSM-Derived Horizontal Tree Biomass Distribution for Deep Learning Biomass Estimation](https://mlsanity.com/paper/2607.05260/) (2026)
- [A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol](https://mlsanity.com/paper/2607.11873/) (2026)

## Abstract (arXiv)

Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall experience. Yet, turning this raw feedback into useful insights is far from straightforward. It has been a long-standing challenge to adopt automatic opinion mining solutions for such education review text data due to the content complexity and low-granularity reporting requirements. Aspect-based Sentiment Analysis (ABSA) offers a promising solution with its rich, sub-sentence-level opinion mining capabilities. However, existing ABSA research and resources are very heavily focused on the commercial domain. In education, they are scarce and hard to develop due to limited public datasets and strict data protection. A high-quality, annotated dataset is urgently needed to advance research in this under-resourced area. In this work, we present EduRABSA (Education Review ABSA), the first public, annotated ABSA education review dataset that covers three review subject types (course, teaching staff, university) in the English language and all main ABSA tasks, including the under-explored implicit aspect and implicit opinion extraction. We also share ASQE-DPT (Data Processing Tool), an offline, lightweight, installation-free manual data annotation tool that generates labelled datasets for comprehensive ABSA tasks from a single-task annotation. Together, these resources contribute to the ABSA community and education domain by removing the dataset barrier, supporting research transparency and reproducibility, and enabling the creation and sharing of further resources. The dataset, annotation tool, and scripts and statistics for dataset processing and sampling are available at https://github.com/yhua219/edurabsa_dataset_and_annotation_tool.