Arabic dialect sentiment swapping requires systems to reverse the affective orientation of an utterance while preserving its core meaning, dialectal form, and fluency. This task is particularly challenging because sentiment may be conveyed through dialect-specific vocabulary, negation patterns, and spelling variation. We present LahjaSensei, our system for Subtask 2 of the DialectSentEval shared task. We investigate two complementary approaches: prompt-based generation with GPT-5.6 and parameter-efficient fine-tuning of Qwen2.5-7B. We evaluate GPT-5.6 under zero-shot, retrieved few-shot, stronger Arabic instructions, and direction-aware configurations. The direction-aware GPT-5.6 pipeline explicitly predicts source polarity before retrieving direction-matched demonstrations and generating the rewrite. For Qwen2.5-7B, we compare pooled QLoRA with linear and TIES merges of direction-specific adapters. Direction-aware GPT-5.6 prompting achieves the highest test sentiment accuracy and remains stable across development and test. Pooled QLoRA produces the highest development BLEU and chrF, while linear merging obtains the highest test BLEU and chrF but lower sentiment accuracy. These findings highlight the central trade-off between sentiment control and preservation of semantic content and demonstrate the value of explicit direction modeling for Arabic dialect sentiment swapping.