Comparing knowledge graphs vs. LLMs for customers’ sentiment analysis of product reviews

We present a novel comparative framework for sentiment analysis in product reviews that combines structured semantic similarity methods with Large Language Models (LLMs). We introduce two new algorithms, iSense and xSense, which leverage lexical knowledge graphs and an ensemble-based strategy to detect product safety issues through semantic proximity. Unlike purely neural models, our approach enables transparent and interpretable sentiment classification aligned with regulatory vocabularies. We evaluate the methods on both synthetic and real-world datasets, including over 1.8 million Amazon reviews in the ”Toys and Games” category. Experimental results demonstrate that xSense not only matches but in some cases surpasses the performance of state-of-the-art LLMs such as BERT, achieving an F1 score of 0.97. These findings establish structured semantic reasoning as a competitive alternative for domain-specific sentiment analysis, with implications for consumer safety analytics and hybrid AI systems.