EduRABSA: An Education Review Dataset for Aspect-based Sentiment
  Analysis Tasks
Authors: name: Yan Cathy Hua, name: Paul Denny, name: Jörg Wicker, name: Katerina Taskova

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.