For our approach to SemEval-2025 Task 11, we employ a multi-tier evaluation framework for perceived emotion analysis. Our system consists of a smaller-parameter-size large language model that independently predicts a given text’s perceived emotion while explaining the reasoning behind its decision. The initial model’s persona is varied through careful prompting, allowing it to represent multiple perspectives. These outputs, including both predictions and reasoning, are aggregated and fed into a final decision-making model that determines the ultimate emotion classification. We evaluated our approach in official SemEval Task 11 on subtasks A and C in all the languages provided.