Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis

• URL: http://arxiv.org/abs/2511.03034v1
• Date: Tue, 04 Nov 2025 22:11:10 GMT
• Title: Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis
• Authors: Yan Cathy Hua, Paul Denny, Jörg Wicker, Katerina Taškova,
• Abstract summary: ABSA research needs are unmet in high-demand yet low-resource areas such as education and healthcare.<n>Traditional evaluation methods based on exact matches are overly rigid for ABSA tasks.<n>We propose a novel evaluation method, Flexible Text Similarity Matching and Optimal Bipartite Pairing.<n>We present the first ABSA study of small decoder-only generative language models.
• Score: 2.3949433621799283
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their categories within a sentence. Despite its rapid growth and broad potential, ABSA research and resources remain concentrated in commercial domains, leaving analytical needs unmet in high-demand yet low-resource areas such as education and healthcare. Domain adaptation challenges and most existing methods' reliance on resource-intensive in-training knowledge injection further hinder progress in these areas. Moreover, traditional evaluation methods based on exact matches are overly rigid for ABSA tasks, penalising any boundary variations which may misrepresent the performance of generative models. This work addresses these gaps through three contributions: 1) We propose a novel evaluation method, Flexible Text Similarity Matching and Optimal Bipartite Pairing (FTS-OBP), which accommodates realistic extraction boundary variations while maintaining strong correlation with traditional metrics and offering fine-grained diagnostics. 2) We present the first ABSA study of small decoder-only generative language models (SLMs; <7B parameters), examining resource lower bounds via a case study in education review ABSA. We systematically explore data-free (in-context learning and weight merging) and data-light fine-tuning methods, and propose a multitask fine-tuning strategy that significantly enhances SLM performance, enabling 1.5-3.8 B models to surpass proprietary large models and approach benchmark results with only 200-1,000 examples on a single GPU. 3) We release the first public set of education review ABSA resources to support future research in low-resource domains.

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