Recent methods for affective analysis have made progress in polarity prediction. However while they work to generate accurate natural language explanations, the transparency and user trust is limited–especially in complex, multi-aspect social media scenarios. To bridge this gap, we propose a method named EMAA(Explainable Multimodal Affective Analysis), a two-stage framework that integrates explanation generation into aspect-based sentiment analysis, combining self-training with direct preference optimization. Additionally, we develop a filter model to automatically evaluate and select high-quality explanations, thereby significantly improving the reliability of training data. Experiments on benchmark datasets show that our method outperforms the baselines in both classification accuracy and explanation quality. Furthermore, we demonstrate that high-quality explanations can improve the robustness and interpretability of sentiment classifications.