This paper presents the IReLIIT(BHU) submission to SemEval-2026 Task 9 for the Chinese language track. We participated in all three subtasks: binary polarization detection,multi-label polarization type classification, and multi-label manifestation identification. Our approach is based on a unified transformer based framework with cross-validation, prediction aggregation, and threshold optimization to improve robustness across tasks. On the official evaluation, our systems achieved Macro-F1 scores of 0.9081, 0.7962, and 0.6484 for Subtasks 1, 2, and 3, respectively on test data.