Exploring Zero-Shot ACSA with Unified Meaning Representation in Chain-of-Thought Prompting
Filippos Ventirozos, Peter Appleby, Matthew Shardlow

TL;DR
This paper introduces a novel Chain-of-Thought prompting method using a Unified Meaning Representation to enhance zero-shot Aspect-Category Sentiment Analysis with large language models, showing promising but model-dependent results.
Contribution
It proposes a UMR-based CoT prompting technique for zero-shot ACSA, addressing data scarcity issues and exploring model performance variations.
Findings
UMR-based approach shows promise with mid-sized models
Performance may depend on the specific language model used
Further research needed for smaller models and generalisability
Abstract
Aspect-Category Sentiment Analysis (ACSA) provides granular insights by identifying specific themes within reviews and their associated sentiment. While supervised learning approaches dominate this field, the scarcity and high cost of annotated data for new domains present significant barriers. We argue that leveraging large language models (LLMs) in a zero-shot setting is a practical alternative where resources for data annotation are limited. In this work, we propose a novel Chain-of-Thought (CoT) prompting technique that utilises an intermediate Unified Meaning Representation (UMR) to structure the reasoning process for the ACSA task. We evaluate this UMR-based approach against a standard CoT baseline across three models (Qwen3-4B, Qwen3-8B, and Gemini-2.5-Pro) and four diverse datasets. Our findings suggest that UMR effectiveness may be model-dependent. Whilst preliminary results…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSentiment Analysis and Opinion Mining · Topic Modeling · Mental Health via Writing
