Emotion attribution in social graphs requires inferring directed emotional attitudes between entities in complex, multi-turn dialogues. While transformer models dominate the field, they often lack the transparency required for social science applications. We present a systematic comparison of three modeling paradigms for this task: a fully interpretable rule-based system, a fine-tuned RoBERTa-large model, and a few-shot Llama-3-8B. Utilizing the DialogRE dataset, we demonstrate that incorporating a 3-turn conversational context significantly improves attribution accuracy across paradigms. Crucially, our results show that the interpretable rule-based system achieves a competitive F1 score, making it statistically indistinguishable from the state-of-the-art RoBERTa model. In contrast, we find that few-shot large language models exhibit poor performance in emotion attribution of semantic relations, performing below the rule-based baseline. We provethat interpretability does not necessitate a performance trade-off in social emotion analysis