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

2025-08-23

AI Summary

The education domain has long suffered from a scarcity of high-quality, publicly available aspect-based sentiment analysis (ABSA) datasets, hindering fine-grained opinion mining from multi-stakeholder textual feedback—such as course, instructor, and university reviews. To address this, we introduce EduRABSA, the first education-specific ABSA dataset, supporting three core tasks: explicit/implicit aspect identification, opinion term extraction, and sentiment classification across the aforementioned review targets. We further propose ASQE-DPT, a lightweight offline annotation tool that enables single-task labeling while automatically generating consistent, reproducible multi-task annotations. All resources—including the annotated dataset, annotation tool, and preprocessing scripts—are publicly released on GitHub. This open infrastructure significantly lowers the barrier to entry for educational text analysis and provides critical foundational support for ABSA research in resource-constrained domains.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Lack of annotated datasets for education review sentiment analysis
• Difficulty in mining low-granularity opinions from educational feedback
• Scarcity of ABSA resources focused on education domain

Innovation

• First public annotated ABSA education dataset
• Offline lightweight manual annotation tool
• Supports implicit aspect and opinion extraction