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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.13575 |
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| _version_ | 1866912834236973056 |
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| author | Nguyen, Thanh-Lam T. Le, Ngoc-Quang Phu, Quoc-Trung Le, Thi-Phuong Pham, Ngoc-Huyen Nguyen, Phuong-Nguyen Le, Hoang-Quynh |
| author_facet | Nguyen, Thanh-Lam T. Le, Ngoc-Quang Phu, Quoc-Trung Le, Thi-Phuong Pham, Ngoc-Huyen Nguyen, Phuong-Nguyen Le, Hoang-Quynh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13575 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Comparing Without Saying: A Dataset and Benchmark for Implicit Comparative Opinion Mining from Same-User Reviews Nguyen, Thanh-Lam T. Le, Ngoc-Quang Phu, Quoc-Trung Le, Thi-Phuong Pham, Ngoc-Huyen Nguyen, Phuong-Nguyen Le, Hoang-Quynh Computation and Language 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. |
| title | Comparing Without Saying: A Dataset and Benchmark for Implicit Comparative Opinion Mining from Same-User Reviews |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.13575 |