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However, in real\u2010world UGC scenarios, accurate identification remains challenging due to highly informal language, the low prevalence of comparative opinions in natural corpora leading to severe class imbalance, and the difficulty of fixed decision thresholds in accommodating fluctuating prediction confidences. In this study, we propose a BERT\u2010based unified framework enhanced with focal loss and a dynamic thresholding mechanism to effectively identify comparative opinions from UGC. The framework integrates three key components. (1) A context\u2010aware pre\u2010training strategy to better adapt to the linguistic characteristics of UGC. (2) A focal loss function to address severe class imbalance. (3) A dynamic threshold adjustment mechanism to refine classification decision boundaries. Experimental results on real\u2010world review datasets demonstrate that the proposed method outperforms baseline models in both accuracy and robustness, offering a practical solution for large\u2010scale comparative opinion detection in noisy online environments. In addition, it provides enterprises with a data\u2010driven tool for competitive analysis.<\/jats:p>","DOI":"10.1111\/exsy.70280","type":"journal-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T06:32:39Z","timestamp":1777962759000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Unified Deep Learning Framework for Identifying Comparative Opinions From User\u2010Generated Content"],"prefix":"10.1111","volume":"43","author":[{"given":"Jie","family":"Lin","sequence":"first","affiliation":[{"name":"School of Economics and Management, Tongji University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Tongji University  Shanghai 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