E-ABSA20K: A Dataset and Propose-and-Verify for Aspect-Based Sentiment Analysis in Long E-commerce Reviews

ACL ARR 2026 January Submission9990 Authors

Keywords: Aspect-Based Sentiment Analysis (ABSA), Large Language Models (LLMs)

Abstract:

Aspect-Based Sentiment Analysis (ABSA) is critical for extracting actionable product insights
from e-commerce reviews. However, most public ABSA benchmarks are restricted to short texts
and a limited range of domains, and therefore underrepresent the challenges posed by
real-world reviews—where multiple aspects co-occur, colloquial and noisy expressions
are common, and evidence must often be aggregated across sentences in long contexts.

We introduce E-ABSA20K, a multi-domain dataset of 20K reviews from four product categories
(Women’s Bags, Dresses, Cosmetics, and Furniture), annotated with review-level
sentiment quads. Compared to existing benchmarks, E-ABSA20K contains substantially
longer and more aspect-dense reviews, averaging 63.9 words and 6.0 quads per review.
We further propose a two-stage propose-and-verify framework for review-level
quadruple extraction (target, aspect, opinion, sentiment). The first stage
generates high-recall candidates under strict schema constraints, while the second
stage conducts explicit grounding, scope, and modality verification, followed by
review-level consolidation to mitigate hallucinations and scope leakage in long
reviews. Experiments across multiple Qwen3 model sizes demonstrate that our
approach consistently outperforms single-stage prompting (with and without
chain-of-thought) as well as competitive ABSA extraction baselines, improving
quad-level micro-F1 and robustness on discourse-hard cases such as comparisons
and conditionals.

Paper Type: Long

Research Area: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Research Area Keywords: Sentiment Analysis, Stylistic Analysis, and Argument Mining, Information Extraction, Resources and Evaluation, Language Modeling, Discourse, Pragmatics, and Reasoning

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 9990