Aspect-Based Sentiment Analysis requires high-quality, detailed datasets to train reliable machine learning 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, a web-based platform that bridges the gap betweenunstructured text and structured knowledge systems, 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 ETL pipeline that aligns collaborative annotations and computes Inter-Annotator Agreement metrics directly at export, producing training-ready datasets without supplementary processing. Validated on 1,002 restaurant reviews, ACAT achieves a median annotation time of 31.58 seconds and a raw inter-annotator agreement of 0.78 to 0.86 in all tasks, demonstrating efficiency and consistent annotation quality across varying levels of annotator expertise.
Interact with the controls below to see how ACAT parses, consolidates, and exports datasets natively into JSON, XML, and CSV. Note how Multi-Annotator exports automatically compute IAA metrics.