arXiv:2606.04189v1 Announce Type: new 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-
The proliferation of advanced AI models necessitates higher quality, more structured datasets for training, driving the development of specialized annotation platforms.
High-quality, collaborative annotation tools are critical for scaling AI development, particularly in nuanced areas like sentiment analysis, which underpins many AI applications.
The ability to efficiently create and manage complex, multi-annotator datasets for aspect-based sentiment analysis will improve model accuracy and reduce development overhead.
- · AI researchers
- · NLP developers
- · Data annotation services
- · Companies using sentiment analysis
- · Manual data consolidation processes
- · Ad-hoc annotation script developers
Improved performance and reliability of AI models trained on higher quality sentiment data.
Increased demand for specialized annotators and annotation workflow expertise as tools become more sophisticated.
Enhanced AI applications across customer service, market research, and content moderation due to better sentiment understanding.
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