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

arXiv 2511.03034 ↗First posted 4 Nov 2025cs.CL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 50 references that are in the map. First posted on arXiv 4 Nov 2025.

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References · 21

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  11. 2024Arcee's MergeKit: A Toolkit for Merging Large Language Modelscited by 189
  12. 2021Aspect Sentiment Quad Prediction as Paraphrase Generationcited by 99
9 more
  1. 2024Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiescited by 67
  2. 2022A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challengescited by 61
  3. 2023On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Trainingcited by 20
  4. 2023A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trendscited by 13
  5. 2024ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment Analysiscited by 13
  6. 2024Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysiscited by 10
  7. 2024A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysiscited by 5
  8. 2025EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Taskscited by 5
  9. 2025Large Language Models for Czech Aspect-Based Sentiment Analysiscited by 1

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