@inproceedings{sun-etal-2026-e,
    title = "{E}-{ABSA}20{K}: A Dataset and Propose-and-Verify for Aspect-Based Sentiment Analysis in Long {E}-commerce Reviews",
    author = "Sun, Tong  and
      Ma, Mingyang  and
      Yu, Cheng",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.892/",
    pages = "17959--17973",
    ISBN = "979-8-89176-395-1",
    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."
}