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

K.M.Tahlil Mahfuz Faruk, Mushfiqur Rahman Talha, H. M. Kawsar Ahamad, Mohammad Galib Shams, Nabil Mosharraf Hossain, Syed Rifat Raiyan, Md Kamrul Hasan, Hasan Mahmud, Riasat Islam

Keywords: Retrieval-Augmented Generation (RAG), Aspect-Based Sentiment Analysis (ABSA), Agentic Chunking, Etiquette-Aware Prompting, Cultural Alignment, Islamic Applications

TL;DR: ADAB is a human-centered AI that generates culturally respectful review responses for Islamic apps, outperforming a baseline LLM with +9.90% overall improvement and strong gains in application specificity.

Abstract: Automated review response systems have advanced considerably, yet most fail to incorporate Islamic etiquette, values, and cultural norms, which are essential for meaningful engagement with users who are adherents of the Islamic faith. Prior research has shown that timely and thoughtful engagement with user reviews can improve user perception. However, managing responses at scale remains a significant challenge for developers, particularly when cultural and religious considerations must be upheld. This research proposes ADAB, a framework for generating review responses that are culturally congruent with Islamic application contexts. The approach integrates a hybrid Retrieval-Augmented Generation (RAG) pipeline that employs agentic chunking and FAISS HNSW indexing to preserve context, combined with aspect-based sentiment analysis (ABSA) for fine-grained understanding of user feedback, and etiquette-aware prompt engineering to ensure responses follow appropriate Islamic decorum. We also introduce a new open-source dataset of Islamic app reviews that supports the system's development and evaluation. 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 specificity (+30.39%). Wilcoxon signed-rank test confirms significant improvements in accuracy (p = 0.0004), relevancy (p = 0.0417), and specificity (p = 8 × 10⁻⁹), while grammatical correctness shows negligible change (p = 0.453). These results demonstrate that embedding cultural alignment in AI systems can foster trust and empathy, charting a path toward more respectful and human-centered response generation.

Track: Track 1: ML on Islamic Content / ML for Muslim Communities

Submission Number: 43