AnnoABSA: A Web-Based Annotation Tool for Aspect-Based Sentiment Analysis with Retrieval-Augmented Suggestions

2026-03-02

AI Summary

This work addresses the limitations of existing aspect-based sentiment analysis (ABSA) annotation tools, which lack support for the full task spectrum, offer limited customizability, and fail to provide context-aware intelligent assistance. To overcome these challenges, we present an open-source web-based annotation platform that, for the first time, enables flexible configuration across the entire ABSA task spectrum. The system innovatively integrates large language models (LLMs) with retrieval-augmented generation (RAG) to deliver dynamic, context-aware suggestions during human-in-the-loop annotation by leveraging semantically similar previously annotated examples. Through few-shot prompting and real-time retrieval, the platform continuously refines suggestion quality as annotation progresses. Released under the MIT license, this tool significantly enhances annotation efficiency and consistency, making it suitable for both academic research and industrial applications.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We introduce AnnoABSA, the first web-based annotation tool to support the full spectrum of Aspect-Based Sentiment Analysis (ABSA) tasks. The tool is highly customizable, enabling flexible configuration of sentiment elements and task-specific requirements. Alongside manual annotation, AnnoABSA provides optional Large Language Model (LLM)-based retrieval-augmented generation (RAG) suggestions that offer context-aware assistance in a human-in-the-loop approach, keeping the human annotator in control. To improve prediction quality over time, the system retrieves the ten most similar examples that are already annotated and adds them as few-shot examples in the prompt, ensuring that suggestions become increasingly accurate as the annotation process progresses. Released as open-source software under the MIT License, AnnoABSA is freely accessible and easily extendable for research and practical applications.

Problem

• Aspect-Based Sentiment Analysis
• Annotation Tool
• Web-Based
• Human-in-the-Loop
• Retrieval-Augmented Generation

Innovation

• Aspect-Based Sentiment Analysis
• Retrieval-Augmented Generation
• Human-in-the-Loop
• Few-Shot Learning
• Web-Based Annotation Tool