@inproceedings{im-etal-2026-oasis,
title = "{OASIS}: An Open-vocabulary Benchmark for Aspect-Opinion-Sentiment Extraction with Implicit-aspect Specificity",
author = "Im, Junyeop and
Park, Jonghyun and
Chae, Dong-Kyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.769/",
pages = "16201--16231",
ISBN = "979-8-89176-462-0",
abstract = "In e-commerce product review mining, Aspect-Based Sentiment Analysis (ABSA) aims to extract fine-grained opinions toward specific aspects in reviews. Existing ABSA benchmarks have expanded from aspect-level sentiment classification to opinion-aware tuple extraction. However, they still provide limited support for evaluating implicit aspects with fine-grained specificity. In particular, implicit aspects are often omitted, collapsed into placeholders, or mapped to predefined categories, which can obscure what reviewers actually evaluate. We introduce OASIS, a review-level Open-vocabulary benchmark for Aspect-Opinion-Sentiment (AOS) extraction with Implicit-aspect Specificity. OASIS contains 6,775 Amazon reviews across eight product domains and 21,054 annotated tuples, where implicit aspects are directly represented as contextually inferred noun phrases rather than closed-set labels. This design yields an implicit-aspect-heavy evaluation setting, with 70.10{\%} of tuples involving implicit aspects. We evaluate a range of open-source and proprietary LLMs under fine-tuned and few-shot settings, showing that full AOS extraction remains challenging even for strong models. We further show that open-vocabulary aspect phrases in OASIS can provide task-useful information. Our benchmark and experimental code is available at: https://github.com/DILAB-HYU/OASIS."
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<abstract>In e-commerce product review mining, Aspect-Based Sentiment Analysis (ABSA) aims to extract fine-grained opinions toward specific aspects in reviews. Existing ABSA benchmarks have expanded from aspect-level sentiment classification to opinion-aware tuple extraction. However, they still provide limited support for evaluating implicit aspects with fine-grained specificity. In particular, implicit aspects are often omitted, collapsed into placeholders, or mapped to predefined categories, which can obscure what reviewers actually evaluate. We introduce OASIS, a review-level Open-vocabulary benchmark for Aspect-Opinion-Sentiment (AOS) extraction with Implicit-aspect Specificity. OASIS contains 6,775 Amazon reviews across eight product domains and 21,054 annotated tuples, where implicit aspects are directly represented as contextually inferred noun phrases rather than closed-set labels. This design yields an implicit-aspect-heavy evaluation setting, with 70.10% of tuples involving implicit aspects. We evaluate a range of open-source and proprietary LLMs under fine-tuned and few-shot settings, showing that full AOS extraction remains challenging even for strong models. We further show that open-vocabulary aspect phrases in OASIS can provide task-useful information. Our benchmark and experimental code is available at: https://github.com/DILAB-HYU/OASIS.</abstract>
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%0 Conference Proceedings
%T OASIS: An Open-vocabulary Benchmark for Aspect-Opinion-Sentiment Extraction with Implicit-aspect Specificity
%A Im, Junyeop
%A Park, Jonghyun
%A Chae, Dong-Kyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F im-etal-2026-oasis
%X In e-commerce product review mining, Aspect-Based Sentiment Analysis (ABSA) aims to extract fine-grained opinions toward specific aspects in reviews. Existing ABSA benchmarks have expanded from aspect-level sentiment classification to opinion-aware tuple extraction. However, they still provide limited support for evaluating implicit aspects with fine-grained specificity. In particular, implicit aspects are often omitted, collapsed into placeholders, or mapped to predefined categories, which can obscure what reviewers actually evaluate. We introduce OASIS, a review-level Open-vocabulary benchmark for Aspect-Opinion-Sentiment (AOS) extraction with Implicit-aspect Specificity. OASIS contains 6,775 Amazon reviews across eight product domains and 21,054 annotated tuples, where implicit aspects are directly represented as contextually inferred noun phrases rather than closed-set labels. This design yields an implicit-aspect-heavy evaluation setting, with 70.10% of tuples involving implicit aspects. We evaluate a range of open-source and proprietary LLMs under fine-tuned and few-shot settings, showing that full AOS extraction remains challenging even for strong models. We further show that open-vocabulary aspect phrases in OASIS can provide task-useful information. Our benchmark and experimental code is available at: https://github.com/DILAB-HYU/OASIS.
%U https://aclanthology.org/2026.findings-emnlp.769/
%P 16201-16231
Markdown (Informal)
[OASIS: An Open-vocabulary Benchmark for Aspect-Opinion-Sentiment Extraction with Implicit-aspect Specificity](https://aclanthology.org/2026.findings-emnlp.769/) (Im et al., Findings 2026)
ACL