\section{Conclusions}
This paper presents the NYCU-NLP system for SemEval-2026 Task 9 on multilingual polarization analysis, detailing both the system architecture and its empirical evaluation. Our approach is built on instruction-tuned small language models (SLMs), including Phi-4, Mistral-small-3.2, and Gemma-3, optimized with LoRA and task-specific prompting strategies, and further strengthened through ensemble-based aggregation mechanisms.

Experimental results show that the stacking ensemble consistently achieved the best performance on both the development and test sets across languages. Specifically, our system obtained macro-averaged F1 scores of 0.8071 for polarization detection (Subtask 1), 0.6108 for polarization type classification (Subtask 2), and 0.5111 for polarization manifestation identification (Subtask 3). Overall, the system ranked first in 15,  second in 12, and third in 10 out of 62 language-specific evaluations, demonstrating the robustness and competitiveness of stacking-based SLM ensembles in multilingual settings.