🤖 AI Summary
This study addresses the lack of efficient, structured collaborative annotation tools in existing Aspect-Based Sentiment Analysis (ABSA) research, which often necessitates cumbersome manual processing for data integration, relation reconstruction, and inter-annotator agreement (IAA) computation. To overcome these limitations, this work proposes the first web-based collaborative annotation platform supporting four ABSA subtasks, featuring an innovative integration of multi-task collaborative annotation and an automated ETL pipeline. The system automatically aligns annotations upon export, preserves character-level positions and dual-span offsets, and computes IAA metrics in real time. Validation on 1,002 restaurant reviews demonstrates a median annotation time of 31.58 seconds per instance, with raw IAA scores ranging from 0.78 to 0.86 across tasks, enabling the direct generation of high-quality, ready-to-use training data and substantially improving both annotation efficiency and structural integrity.
📝 Abstract
Aspect-Based Sentiment Analysis (ABSA) requires high-quality datasets to train reliable models. However, existing annotation tools treat output as flat files, leaving researchers to manually consolidate multi-annotator data, reconstruct relational structures, and compute reliability metrics through custom scripts. This paper introduces ACAT (Aspect-based sentiment analysis Collaborative Annotation Tool), a web-based platform natively supporting four ABSA workflows: (1) Aspect-Category Sentiment Analysis, (2) Clause-Level Segmentation, (3) Aspect-Term Sentiment Analysis with character-level position tracking, and (4) Aspect Sentiment Triplet Extraction with dual span offset preservation. Its core contribution is an automated Extract, Transform, Load (ETL) pipeline that aligns collaborative annotations and computes Inter-Annotator Agreement (IAA) metrics directly at export, yielding training-ready datasets. In a preliminary validation on 1,002 restaurant reviews with two annotators of differing expertise, ACAT achieves a median annotation time of 31.58 seconds and a raw IAA ranging from 0.78 to 0.86 across all tasks.