首个教育评论方面情感分析数据集，助力自动挖掘学生反馈。
EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks
- 构建首个英文教育评论ABSA数据集，覆盖课程、教师、学校三类主体。
- 支持显式与隐式方面/观点抽取，填补教育领域研究空白。
- 提供免安装标注工具，降低数据标注门槛，适合教育研究者使用。
每年，各类教育机构都会收到大量学生关于课程、教学和整体体验的文本反馈。然而，将这些原始反馈转化为有用洞察仍面临挑战，主要因内容复杂且需细粒度分析。基于方面的情感分析（ABSA）能实现子句级别的意见挖掘，是潜在解决方案。但现有研究和资源高度集中于商业领域，教育领域缺乏公开数据集，且受数据保护限制难以构建高质量标注数据。为此，本文提出EduRABSA——首个公开的英文教育评论方面情感分析数据集，涵盖课程、教师、大学三类主题，支持全部主流ABSA任务，包括尚未充分研究的隐式方面与隐式观点抽取。同时发布ASQE-DPT数据处理工具，一款无需安装的轻量级手动标注工具，可从单任务标注生成多任务标签数据集。上述资源有助于消除数据壁垒，提升研究透明性与可复现性，并推动更多教育资源的创建与共享。数据集、工具及处理脚本已开源，地址：https://github.com/yhua219/edurabsa_dataset_and_annotation_tool。
原文摘要 · Abstract (English)
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.
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