ACAT: A Collaborative Platform for Efficient Aspect-Based Sentiment Dataset Annotation
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Computer Science > Computation and Language
Title:ACAT: A Collaborative Platform for Efficient Aspect-Based Sentiment Dataset Annotation
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.
| Comments: | Accepted at The 28th International Conference on Big Data Analytics and Knowledge Discovery (DaWak 2026) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.04189 [cs.CL] |
| (or arXiv:2606.04189v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04189
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Ana-Maria Luisa Mocanu [view email][v1] Tue, 2 Jun 2026 20:16:54 UTC (89 KB)
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