ADAB: A Culturally-Aligned Automated Response Generation Framework for Islamic App Reviews by Integrating ABSA and Hybrid RAG

Abstract

Automated review response systems have advanced considerably, yet most fail to in corporate Islamic etiquette, values, and cultural norms, essential for meaningful en gagement with Muslim users. Prior research has shown that timely and thoughtful engagement with user reviews can improve user perception. However, managing re sponses at scale remains a significant challenge for developers, particularly when cul tural and religious considerations must be upheld. This research proposes ADAB, a framework forgenerating review responses that are culturally congruent with Islamic application contexts. The approach integrates a hybrid Retrieval-Augmented Gen eration (RAG) pipeline that employs agentic chunking and FAISS HNSW indexing to preserve contexts, with aspect-based sentiment analysis (ABSA) for fine-grained understanding of user feedback, and etiquette-aware prompt engineering to imbue responses with appropriate Islamic decorum. We also introduce a new open-source dataset of Islamic app reviews that supports the system’sdevelopmentandevaluation. Direct pairwise comparisons showed that ADAB’s responses were preferred in 40% of cases, compared to 15.3% for the baseline, with 44.7% ties. On average, our approach achieves an overall improvement of 9.9%, with the largest gain in application speci ficity (+30.39%). Wilcoxon signed-rank test confirmed significant improvements in accuracy (𝑝 = 0.0004), relevancy (𝑝 = 0.0417), and specificity (𝑝 = 8 × 10−9), while grammaticalcorrectnessshowed negligiblechange(𝑝 = 0.453). Theseresultsdemon strate that embedding cultural alignment in AI systems can foster trust and empathy, charting a path toward more respectful and human-centered response generation.