L3IA at AraSentEval 2026 Subtask 2: LLM-Based Multi-Step Pipeline for Arabic Sentiment Swap

Abstract

This paper describes our system submitted to the AraSentEval 2026 Shared Task, Subtask 2: Arabic Sentiment Swap. The task requires rewriting Arabic sentences to invert their sentiment polarity while preserving the core meaning. We propose a multi-step pipeline approach that uses large language models (LLMs). Our method decomposes the sentiment inversion problem into three stages: (1) sentiment expression extraction, where the model identifies all sentiment-bearing words and phrases in the input sentence; (2) opposite expression generation, where each identified expression is replaced by its semantic opposite; and (3) sentence reconstruction, where the final output is assembled to ensure grammatical correctness and natural fluency. Our system achieves 74.3% sentiment style accuracy, 27.22 BLEU, and 55.04 chrF on the official test set.

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