Enhancing Recommendation Systems via Aspect-Level Sentiment Analysis for Identifying Unreliable Reviews - El Mehdi Lghaouch; Fatima Zahra Abbour; Soumaya Ounacer; Soufiane Ardchir; Mohamed Azzouazi (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2026)
User-generated reviews are an important source of information for e-commerce recommendation systems. However, reviews may contain conflicting opinions across product aspects such as quality, price, and delivery, which can weaken both review-level trust and aspect-level signals. To address this issue, we propose AspectGuard, a filtering framework based on Aspect-Based Sentiment Analysis (ABSA) and a Sentiment Divergence Score (SDS) that quantifies disagreement across aspect-level sentiments within a review. Reviews with high divergence are identified as potentially unreliable and excluded before constructing recommendation profiles. To isolate the contribution of the proposed filtering mechanism, we compare the same content-based recommendation model with and without AspectGuard filtering. Experiments conducted on a subset of the Amazon Reviews 2023 Electronics dataset show that the proposed filtering strategy improves both rating prediction and ranking quality. These results indicate that aspect level sentiment divergence can serve as an effective signal for identifying unreliable reviews and improving the reliability of recommendation systems.