This work investigates multilingual emotion analysis through two complementary tasks: multi-label emotion classification and emotion-conditioned text generation. The study focuses on English and Spanish, with Portuguese included in the transfer experiments, to examine how cross-lingual training strategies can improve performance in low-resource set- tings. We compare monolingual training, mixed-language training, and sequential transfer, and show that even limited target-language supervision can substantially improve classification results. In particular, multilingual models and parameter-efficient adaptation with Llama-3.2-3B achieve strong cross-lingual generalization, especially for Spanish. The second part of the study extends the analysis to emotion-conditioned generation. Using a prompt-based approach, we evaluate whether a language model can be steered to produce text aligned with a target emotion. To measure this alignment at scale, we reuse the trained classifier as an automatic evaluator. The results show that few-shot prompt- ing slightly improves emotional control, while zero-shot generation produces more diverse outputs. Overall, the findings suggest that cross-lingual transfer can reduce annotation requirements in multilingual emotion analysis and that classifier-based evaluation provides a practical way to assess emotion-conditioned generation.