Emotion recognition in text is crucial in natural language processing but challenging in multilingual settings due to varying cultural and linguistic cues. In this study, we assess the zero-shot capability of GPT-4o Mini, a cost-efficient small-scale LLM, for multilingual emotion detection. Since small LLMs tend to perform better with task decomposition, we introduce a two-step approach: (1) Role-Play Rewriting, where the model minimally rewrites the input sentence to reflect different emotional tones, and (2) Contrastive Judging, where the original sentence is compared against these rewrites to determine the most suitable emotion label. Our approach requires no labeled data for fine-tuning or few-shot in-context learning, enabling a plug-and-play solution that can seamlessly integrate with any LLM. Results show promising performance, particularly in low-resource languages, though with a performance gap between high- and low-resource settings. These findings highlight how task decomposition techniques like role-play and contrastive judging can enhance small LLMs’ zero-shot capabilities for real-world, data-scarce scenarios.