Comparing Without Saying: A Dataset and Benchmark for Implicit Comparative Opinion Mining from Same-User Reviews

• URL: http://arxiv.org/abs/2601.13575v1
• Date: Tue, 20 Jan 2026 04:00:51 GMT
• Title: Comparing Without Saying: A Dataset and Benchmark for Implicit Comparative Opinion Mining from Same-User Reviews
• Authors: Thanh-Lam T. Nguyen, Ngoc-Quang Le, Quoc-Trung Phu, Thi-Phuong Le, Ngoc-Huyen Pham, Phuong-Nguyen Nguyen, Hoang-Quynh Le,
• Abstract summary: We introduce SUDO, a novel dataset for implicit comparative opinion mining from same-user reviews.<n> SUDO comprises 4,150 annotated review pairs (15,191 sentences) with a bi-level structure capturing aspect-level mentions and review-level preferences.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing studies on comparative opinion mining have mainly focused on explicit comparative expressions, which are uncommon in real-world reviews. This leaves implicit comparisons - here users express preferences across separate reviews - largely underexplored. We introduce SUDO, a novel dataset for implicit comparative opinion mining from same-user reviews, allowing reliable inference of user preferences even without explicit comparative cues. SUDO comprises 4,150 annotated review pairs (15,191 sentences) with a bi-level structure capturing aspect-level mentions and review-level preferences. We benchmark this task using two baseline architectures: traditional machine learning- and language model-based baselines. Experimental results show that while the latter outperforms the former, overall performance remains moderate, revealing the inherent difficulty of the task and establishing SUDO as a challenging and valuable benchmark for future research.

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